Alexander Panchenko


2023

pdf bib
Answer Candidate Type Selection: Text-To-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs
Mikhail Salnikov | Maria Lysyuk | Pavel Braslavski | Anton Razzhigaev | Valentin A. Malykh | Alexander Panchenko
Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)

pdf bib
Error syntax aware augmentation of feedback comment generation dataset
Nikolay Babakov | Maria Lysyuk | Alexander Shvets | Lilya Kazakova | Alexander Panchenko
Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges

This paper presents a solution to the GenChal 2022 shared task dedicated to feedback comment generation for writing learning. In terms of this task given a text with an error and a span of the error, a system generates an explanatory note that helps the writer (language learner) to improve their writing skills. Our solution is based on fine-tuning the T5 model on the initial dataset augmented according to syntactical dependencies of the words located within indicated error span. The solution of our team ‘nigula’ obtained second place according to manual evaluation by the organizers.

pdf bib
Efficient Out-of-Domain Detection for Sequence to Sequence Models
Artem Vazhentsev | Akim Tsvigun | Roman Vashurin | Sergey Petrakov | Daniil Vasilev | Maxim Panov | Alexander Panchenko | Artem Shelmanov
Findings of the Association for Computational Linguistics: ACL 2023

Sequence-to-sequence (seq2seq) models based on the Transformer architecture have become a ubiquitous tool applicable not only to classical text generation tasks such as machine translation and summarization but also to any other task where an answer can be represented in a form of a finite text fragment (e.g., question answering). However, when deploying a model in practice, we need not only high performance but also an ability to determine cases where the model is not applicable. Uncertainty estimation (UE) techniques provide a tool for identifying out-of-domain (OOD) input where the model is susceptible to errors. State-of-the-art UE methods for seq2seq models rely on computationally heavyweight and impractical deep ensembles. In this work, we perform an empirical investigation of various novel UE methods for large pre-trained seq2seq models T5 and BART on three tasks: machine translation, text summarization, and question answering. We apply computationally lightweight density-based UE methods to seq2seq models and show that they often outperform heavyweight deep ensembles on the task of OOD detection.

pdf bib
Predicting Terms in IS-A Relations with Pre-trained Transformers
Irina Nikishina | Polina Chernomorchenko | Anastasiia Demidova | Alexander Panchenko | Chris Biemann
Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)

pdf bib
Kandinsky: An Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion
Anton Razzhigaev | Arseniy Shakhmatov | Anastasia Maltseva | Vladimir Arkhipkin | Igor Pavlov | Ilya Ryabov | Angelina Kuts | Alexander Panchenko | Andrey Kuznetsov | Denis Dimitrov
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Text-to-image generation is a significant domain in modern computer vision and achieved substantial improvements through the evolution of generative architectures. Among these, diffusion-based models demonstrated essential quality enhancements. These models generally split into two categories: pixel-level and latent-level approaches. We present Kandinsky – a novel exploration of latent diffusion architecture, combining the principles of image prior models with latent diffusion techniques. The image prior model, is trained separately to map CLIP text and image embeddings. Another distinct feature of the proposed model is the modified MoVQ implementation, which serves as the image autoencoder component. Overall the designed model contains 3.3B parameters. We also deployed a user-friendly demo system that supports diverse generative modes such as text-to-image generation, image fusion, text and image fusion, image variations generation and text-guided inpainting/outpainting. Additionally we released the source code and checkpoints for Kandinsky models. Experimental evaluations demonstrate FID score of 8.03 on the COCO-30K dataset, marking our model as the top open source performer in terms of measurable image generation quality.

pdf bib
LM-Polygraph: Uncertainty Estimation for Language Models
Ekaterina Fadeeva | Roman Vashurin | Akim Tsvigun | Artem Vazhentsev | Sergey Petrakov | Kirill Fedyanin | Daniil Vasilev | Elizaveta Goncharova | Alexander Panchenko | Maxim Panov | Timothy Baldwin | Artem Shelmanov
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields. However, a significant challenge arises as these models often “hallucinate”, i.e., fabricate facts without providing users an apparent means to discern the veracity of their statements. Uncertainty estimation (UE) methods are one path to safer, more responsible, and more effective use of LLMs. However, to date, research on UE methods for LLMs has been focused primarily on theoretical rather than engineering contributions. In this work, we tackle this issue by introducing LM-Polygraph, a framework with implementations of a battery of state-of-the-art UE methods for LLMs in text generation tasks, with unified program interfaces in Python. Additionally, it introduces an extendable benchmark for consistent evaluation of UE techniques by researchers, and a demo web application that enriches the standard chat dialog with confidence scores, empowering end-users to discern unreliable responses. LM-Polygraph is compatible with the most recent LLMs, including BLOOMz, LLaMA-2, ChatGPT, and GPT-4, and is designed to support future releases of similarly-styled LMs.

pdf bib
Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability?
Gleb Kuzmin | Artem Vazhentsev | Artem Shelmanov | Xudong Han | Simon Suster | Maxim Panov | Alexander Panchenko | Timothy Baldwin
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Exploring Methods for Cross-lingual Text Style Transfer: The Case of Text Detoxification
Daryna Dementieva | Daniil Moskovskiy | David Dale | Alexander Panchenko
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Graph-Enriched Biomedical Language Models: A Research Proposal
Andrey Sakhovskiy | Alexander Panchenko | Elena Tutubalina
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Student Research Workshop

pdf bib
Team NLLG submission for Eval4NLP 2023 Shared Task: Retrieval-Augmented In-Context Learning for NLG Evaluation
Daniil Larionov | Vasiliy Viskov | George Kokush | Alexander Panchenko | Steffen Eger
Proceedings of the 4th Workshop on Evaluation and Comparison of NLP Systems

In this paper, we propose a retrieval-augmented in-context learning for natural language generation (NLG) evaluation. This method allows practitioners to utilize large language models (LLMs) for various NLG evaluation tasks without any fine-tuning. We apply our approach to Eval4NLP 2023 Shared Task in translation evaluation and summarization evaluation subtasks. The findings suggest that retrieval-augmented in-context learning is a promising approach for creating LLM-based evaluation metrics for NLG. Further research directions include exploring the performance of various publicly available LLM models and identifying which LLM properties help boost the quality of the metric.

pdf bib
Detecting Text Formality: A Study of Text Classification Approaches
Daryna Dementieva | Nikolay Babakov | Alexander Panchenko
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

Formality is one of the important characteristics of text documents. The automatic detection of the formality level of a text is potentially beneficial for various natural language processing tasks. Before, two large-scale datasets were introduced for multiple languages featuring formality annotation—GYAFC and X-FORMAL. However, they were primarily used for the training of style transfer models. At the same time, the detection of text formality on its own may also be a useful application. This work proposes the first to our knowledge systematic study of formality detection methods based on statistical, neural-based, and Transformer-based machine learning methods and delivers the best-performing models for public usage. We conducted three types of experiments – monolingual, multilingual, and cross-lingual. The study shows the overcome of Char BiLSTM model over Transformer-based ones for the monolingual and multilingual formality classification task, while Transformer-based classifiers are more stable to cross-lingual knowledge transfer.

pdf bib
Semantically-Informed Regressive Encoder Score
Vasiliy Viskov | George Kokush | Daniil Larionov | Steffen Eger | Alexander Panchenko
Proceedings of the Eighth Conference on Machine Translation

Machine translation is natural language generation (NLG) problem of translating source text from one language to another. As every task in machine learning domain it requires to have evaluation metric. The most obvious one is human evaluation but it is expensive in case of money and time consumption. In last years with appearing of pretrained transformer architectures and large language models (LLMs) state-of-the-art results in automatic machine translation evaluation got a huge quality step in terms of correlation with expert assessment. We introduce MRE-Score, seMantically-informed Regression Encoder Score, the approach with constructing automatic machine translation evaluation system based on regression encoder and contrastive pretraining for downstream problem.

pdf bib
Efficient GPT Model Pre-training using Tensor Train Matrix Representation
Viktoriia Chekalina | Georgiy Novikov | Julia Gusak | Alexander Panchenko | Ivan Oseledets
Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation

pdf bib
Large Language Models Meet Knowledge Graphs to Answer Factoid Questions
Mikhail Salnikov | Hai Le | Prateek Rajput | Irina Nikishina | Pavel Braslavski | Valentin Malykh | Alexander Panchenko
Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation

pdf bib
A Computational Study of Matrix Decomposition Methods for Compression of Pre-trained Transformers
Sergey Pletenev | Viktoriia Chekalina | Daniil Moskovskiy | Mikhail Seleznev | Sergey Zagoruyko | Alexander Panchenko
Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation

pdf bib
Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks
Artem Vazhentsev | Gleb Kuzmin | Akim Tsvigun | Alexander Panchenko | Maxim Panov | Mikhail Burtsev | Artem Shelmanov
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Many text classification tasks are inherently ambiguous, which results in automatic systems having a high risk of making mistakes, in spite of using advanced machine learning models. For example, toxicity detection in user-generated content is a subjective task, and notions of toxicity can be annotated according to a variety of definitions that can be in conflict with one another. Instead of relying solely on automatic solutions, moderation of the most difficult and ambiguous cases can be delegated to human workers. Potential mistakes in automated classification can be identified by using uncertainty estimation (UE) techniques. Although UE is a rapidly growing field within natural language processing, we find that state-of-the-art UE methods estimate only epistemic uncertainty and show poor performance, or under-perform trivial methods for ambiguous tasks such as toxicity detection. We argue that in order to create robust uncertainty estimation methods for ambiguous tasks it is necessary to account also for aleatoric uncertainty. In this paper, we propose a new uncertainty estimation method that combines epistemic and aleatoric UE methods. We show that by using our hybrid method, we can outperform state-of-the-art UE methods for toxicity detection and other ambiguous text classification tasks.

pdf bib
A System for Answering Simple Questions in Multiple Languages
Anton Razzhigaev | Mikhail Salnikov | Valentin Malykh | Pavel Braslavski | Alexander Panchenko
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

Our research focuses on the most prevalent type of queries— simple questions —exemplified by questions like “What is the capital of France?”. These questions reference an entity such as “France”, which is directly connected (one hop) to the answer entity “Paris” in the underlying knowledge graph (KG). We propose a multilingual Knowledge Graph Question Answering (KGQA) technique that orders potential responses based on the distance between the question’s text embeddings and the answer’s graph embeddings. A system incorporating this novel method is also described in our work. Through comprehensive experimentation using various English and multilingual datasets and two KGs — Freebase and Wikidata — we illustrate the comparative advantage of the proposed method across diverse KG embeddings and languages. This edge is apparent even against robust baseline systems, including seq2seq QA models, search-based solutions and intricate rule-based pipelines. Interestingly, our research underscores that even advanced AI systems like ChatGPT encounter difficulties when tasked with answering simple questions. This finding emphasizes the relevance and effectiveness of our approach, which consistently outperforms such systems. We are making the source code and trained models from our study publicly accessible to promote further advancements in multilingual KGQA.

2022

pdf bib
Active Learning for Abstractive Text Summarization
Akim Tsvigun | Ivan Lysenko | Danila Sedashov | Ivan Lazichny | Eldar Damirov | Vladimir Karlov | Artemy Belousov | Leonid Sanochkin | Maxim Panov | Alexander Panchenko | Mikhail Burtsev | Artem Shelmanov
Findings of the Association for Computational Linguistics: EMNLP 2022

Construction of human-curated annotated datasets for abstractive text summarization (ATS) is very time-consuming and expensive because creating each instance requires a human annotator to read a long document and compose a shorter summary that would preserve the key information relayed by the original document. Active Learning (AL) is a technique developed to reduce the amount of annotation required to achieve a certain level of machine learning model performance. In information extraction and text classification, AL can reduce the amount of labor up to multiple times. Despite its potential for aiding expensive annotation, as far as we know, there were no effective AL query strategies for ATS. This stems from the fact that many AL strategies rely on uncertainty estimation, while as we show in our work, uncertain instances are usually noisy, and selecting them can degrade the model performance compared to passive annotation. We address this problem by proposing the first effective query strategy for AL in ATS based on diversity principles. We show that given a certain annotation budget, using our strategy in AL annotation helps to improve the model performance in terms of ROUGE and consistency scores. Additionally, we analyze the effect of self-learning and show that it can additionally increase the performance of the model.

pdf bib
TaxFree: a Visualization Tool for Candidate-free Taxonomy Enrichment
Irina Nikishina | Ivan Andrianov | Alsu Vakhitova | Alexander Panchenko
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations

Taxonomies are widely used in a various number of downstream NLP tasks and, therefore, should be kept up-to-date. In this paper, we present TaxFree, an open source system for taxonomy visualisation and automatic Taxonomy Enrichment without pre-defined candidates on the example of WordNet-3.0. As oppose to the traditional task formulation (where the list of new words is provided beforehand), we provide an approach for automatic extension of a taxonomy using a large pre-trained language model. As an advantage to the existing visualisation tools of WordNet, TaxFree also integrates graphic representations of synsets from ImageNet. Such visualisation tool can be used for both updating taxonomies and inspecting them for the required modifications.

pdf bib
A Study on Manual and Automatic Evaluation for Text Style Transfer: The Case of Detoxification
Varvara Logacheva | Daryna Dementieva | Irina Krotova | Alena Fenogenova | Irina Nikishina | Tatiana Shavrina | Alexander Panchenko
Proceedings of the 2nd Workshop on Human Evaluation of NLP Systems (HumEval)

It is often difficult to reliably evaluate models which generate text. Among them, text style transfer is a particularly difficult to evaluate, because its success depends on a number of parameters. We conduct an evaluation of a large number of models on a detoxification task. We explore the relations between the manual and automatic metrics and find that there is only weak correlation between them, which is dependent on the type of model which generated text. Automatic metrics tend to be less reliable for better-performing models. However, our findings suggest that, ChrF and BertScore metrics can be used as a proxy for human evaluation of text detoxification to some extent.

pdf bib
RuPAWS: A Russian Adversarial Dataset for Paraphrase Identification
Nikita Martynov | Irina Krotova | Varvara Logacheva | Alexander Panchenko | Olga Kozlova | Nikita Semenov
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Paraphrase identification task can be easily challenged by changing word order, e.g. as in “Can a good person become bad?”. While for English this problem was tackled by the PAWS dataset (Zhang et al., 2019), datasets for Russian paraphrase detection lack non-paraphrase examples with high lexical overlap. We present RuPAWS, the first adversarial dataset for Russian paraphrase identification. Our dataset consists of examples from PAWS translated to the Russian language and manually annotated by native speakers. We compare it to the largest available dataset for Russian ParaPhraser and show that the best available paraphrase identifiers for the Russian language fail on the RuPAWS dataset. At the same time, the state-of-the-art paraphrasing model RuBERT trained on both RuPAWS and ParaPhraser obtains high performance on the RuPAWS dataset while maintaining its accuracy on the ParaPhraser benchmark. We also show that RuPAWS can measure the sensitivity of models to word order and syntax structure since simple baselines fail even when given RuPAWS training samples.

pdf bib
Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing
Dmitry Ustalov | Yanjun Gao | Alexander Panchenko | Marco Valentino | Mokanarangan Thayaparan | Thien Huu Nguyen | Gerald Penn | Arti Ramesh | Abhik Jana
Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing

pdf bib
Cross-Modal Contextualized Hidden State Projection Method for Expanding of Taxonomic Graphs
Irina Nikishina | Alsu Vakhitova | Elena Tutubalina | Alexander Panchenko
Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing

Taxonomy is a graph of terms organized hierarchically using is-a (hypernymy) relations. We suggest novel candidate-free task formulation for the taxonomy enrichment task. To solve the task, we leverage lexical knowledge from the pre-trained models to predict new words missing in the taxonomic resource. We propose a method that combines graph-, and text-based contextualized representations from transformer networks to predict new entries to the taxonomy. We have evaluated the method suggested for this task against text-only baselines based on BERT and fastText representations. The results demonstrate that incorporation of graph embedding is beneficial in the task of hyponym prediction using contextualized models. We hope the new challenging task will foster further research in automatic text graph construction methods.

pdf bib
ParaDetox: Detoxification with Parallel Data
Varvara Logacheva | Daryna Dementieva | Sergey Ustyantsev | Daniil Moskovskiy | David Dale | Irina Krotova | Nikita Semenov | Alexander Panchenko
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We release two parallel corpora which can be used for the training of detoxification models. To the best of our knowledge, these are the first parallel datasets for this task. We describe our pipeline in detail to make it fast to set up for a new language or domain, thus contributing to faster and easier development of new parallel resources. We train several detoxification models on the collected data and compare them with several baselines and state-of-the-art unsupervised approaches. We conduct both automatic and manual evaluations. All models trained on parallel data outperform the state-of-the-art unsupervised models by a large margin. This suggests that our novel datasets can boost the performance of detoxification systems.

pdf bib
Uncertainty Estimation of Transformer Predictions for Misclassification Detection
Artem Vazhentsev | Gleb Kuzmin | Artem Shelmanov | Akim Tsvigun | Evgenii Tsymbalov | Kirill Fedyanin | Maxim Panov | Alexander Panchenko | Gleb Gusev | Mikhail Burtsev | Manvel Avetisian | Leonid Zhukov
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc. Most of the works on modeling the uncertainty of deep neural networks evaluate these methods on image classification tasks. Little attention has been paid to UE in natural language processing. To fill this gap, we perform a vast empirical investigation of state-of-the-art UE methods for Transformer models on misclassification detection in named entity recognition and text classification tasks and propose two computationally efficient modifications, one of which approaches or even outperforms computationally intensive methods.

pdf bib
A large-scale computational study of content preservation measures for text style transfer and paraphrase generation
Nikolay Babakov | David Dale | Varvara Logacheva | Alexander Panchenko
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Text style transfer and paraphrasing of texts are actively growing areas of NLP, dozens of methods for solving these tasks have been recently introduced. In both tasks, the system is supposed to generate a text which should be semantically similar to the input text. Therefore, these tasks are dependent on methods of measuring textual semantic similarity. However, it is still unclear which measures are the best to automatically evaluate content preservation between original and generated text. According to our observations, many researchers still use BLEU-like measures, while there exist more advanced measures including neural-based that significantly outperform classic approaches. The current problem is the lack of a thorough evaluation of the available measures. We close this gap by conducting a large-scale computational study by comparing 57 measures based on different principles on 19 annotated datasets. We show that measures based on cross-encoder models outperform alternative approaches in almost all cases. We also introduce the Mutual Implication Score (MIS), a measure that uses the idea of paraphrasing as a bidirectional entailment and outperforms all other measures on the paraphrase detection task and performs on par with the best measures in the text style transfer task.

pdf bib
Exploring Cross-lingual Text Detoxification with Large Multilingual Language Models.
Daniil Moskovskiy | Daryna Dementieva | Alexander Panchenko
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Detoxification is a task of generating text in polite style while preserving meaning and fluency of the original toxic text. Existing detoxification methods are monolingual i.e. designed to work in one exact language. This work investigates multilingual and cross-lingual detoxification and the behavior of large multilingual models in this setting. Unlike previous works we aim to make large language models able to perform detoxification without direct fine-tuning in a given language. Experiments show that multilingual models are capable of performing multilingual style transfer. However, tested state-of-the-art models are not able to perform cross-lingual detoxification and direct fine-tuning on exact language is currently inevitable and motivating the need of further research in this direction.

pdf bib
MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering
Viktoriia Chekalina | Anton Razzhigaev | Albert Sayapin | Evgeny Frolov | Alexander Panchenko
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Knowledge Graphs (KGs) are symbolically structured storages of facts. The KG embedding contains concise data used in NLP tasks requiring implicit information about the real world. Furthermore, the size of KGs that may be useful in actual NLP assignments is enormous, and creating embedding over it has memory cost issues. We represent KG as a 3rd-order binary tensor and move beyond the standard CP decomposition (CITATION) by using a data-specific generalized version of it (CITATION). The generalization of the standard CP-ALS algorithm allows obtaining optimization gradients without a backpropagation mechanism. It reduces the memory needed in training while providing computational benefits. We propose a MEKER, a memory-efficient KG embedding model, which yields SOTA-comparable performance on link prediction tasks and KG-based Question Answering.

pdf bib
SkoltechNLP at SemEval-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector Representations
Mikhail Kuimov | Daryna Dementieva | Alexander Panchenko
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This paper describes our contribution to SemEval 2022 Task 8: Multilingual News Article Similarity. The aim was to test completely different approaches and distinguish the best performing. That is why we’ve considered systems based on Transformer-based encoders, NER-based, and NLI-based methods (and their combination with SVO dependency triplets representation). The results prove that Transformer models produce the best scores. However, there is space for research and approaches that give not yet comparable but more interpretable results.

pdf bib
Pixel-Level BPE for Auto-Regressive Image Generation
Anton Razzhigaev | Anton Voronov | Andrey Kaznacheev | Andrey Kuznetsov | Denis Dimitrov | Alexander Panchenko
Proceedings of the First Workshop on Performance and Interpretability Evaluations of Multimodal, Multipurpose, Massive-Scale Models

Pixel-level autoregression with Transformer models (Image GPT or iGPT) is one of the recent approaches to image generation that has not received massive attention and elaboration due to quadratic complexity of attention as it imposes huge memory requirements and thus restricts the resolution of the generated images. In this paper, we propose to tackle this problem by adopting Byte-Pair-Encoding (BPE) originally proposed for text processing to the image domain to drastically reduce the length of the modeled sequence. The obtained results demonstrate that it is possible to decrease the amount of computation required to generate images pixel-by-pixel while preserving their quality and the expressiveness of the features extracted from the model. Our results show that there is room for improvement for iGPT-like models with more thorough research on the way to the optimal sequence encoding techniques for images.

2021

pdf bib
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates
Artem Shelmanov | Dmitri Puzyrev | Lyubov Kupriyanova | Denis Belyakov | Daniil Larionov | Nikita Khromov | Olga Kozlova | Ekaterina Artemova | Dmitry V. Dylov | Alexander Panchenko
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Annotating training data for sequence tagging of texts is usually very time-consuming. Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to significantly reduce the necessary annotation budget. We are the first to thoroughly investigate this powerful combination for the sequence tagging task. We conduct an extensive empirical study of various Bayesian uncertainty estimation methods and Monte Carlo dropout options for deep pre-trained models in the active learning framework and find the best combinations for different types of models. Besides, we also demonstrate that to acquire instances during active learning, a full-size Transformer can be substituted with a distilled version, which yields better computational performance and reduces obstacles for applying deep active learning in practice.

pdf bib
How Certain is Your Transformer?
Artem Shelmanov | Evgenii Tsymbalov | Dmitri Puzyrev | Kirill Fedyanin | Alexander Panchenko | Maxim Panov
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

In this work, we consider the problem of uncertainty estimation for Transformer-based models. We investigate the applicability of uncertainty estimates based on dropout usage at the inference stage (Monte Carlo dropout). The series of experiments on natural language understanding tasks shows that the resulting uncertainty estimates improve the quality of detection of error-prone instances. Special attention is paid to the construction of computationally inexpensive estimates via Monte Carlo dropout and Determinantal Point Processes.

pdf bib
Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language
Viktoriia Chekalina | Alexander Bondarenko | Chris Biemann | Meriem Beloucif | Varvara Logacheva | Alexander Panchenko
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

We present a system for answering comparative questions (Is X better than Y with respect to Z?) in natural language. Answering such questions is important for assisting humans in making informed decisions. The key component of our system is a natural language interface for comparative QA that can be used in personal assistants, chatbots, and similar NLP devices. Comparative QA is a challenging NLP task, since it requires collecting support evidence from many different sources, and direct comparisons of rare objects may be not available even on the entire Web. We take the first step towards a solution for such a task offering a testbed for comparative QA in natural language by probing several methods, making the three best ones available as an online demo.

pdf bib
Documents Representation via Generalized Coupled Tensor Chain with the Rotation Group constraint
Igor Vorona | Anh-Huy Phan | Alexander Panchenko | Andrzej Cichocki
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

pdf bib
Text Detoxification using Large Pre-trained Neural Models
David Dale | Anton Voronov | Daryna Dementieva | Varvara Logacheva | Olga Kozlova | Nikita Semenov | Alexander Panchenko
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We present two novel unsupervised methods for eliminating toxicity in text. Our first method combines two recent ideas: (1) guidance of the generation process with small style-conditional language models and (2) use of paraphrasing models to perform style transfer. We use a well-performing paraphraser guided by style-trained language models to keep the text content and remove toxicity. Our second method uses BERT to replace toxic words with their non-offensive synonyms. We make the method more flexible by enabling BERT to replace mask tokens with a variable number of words. Finally, we present the first large-scale comparative study of style transfer models on the task of toxicity removal. We compare our models with a number of methods for style transfer. The models are evaluated in a reference-free way using a combination of unsupervised style transfer metrics. Both methods we suggest yield new SOTA results.

pdf bib
SkoltechNLP at SemEval-2021 Task 2: Generating Cross-Lingual Training Data for the Word-in-Context Task
Anton Razzhigaev | Nikolay Arefyev | Alexander Panchenko
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

In this paper, we present a system for the solution of the cross-lingual and multilingual word-in-context disambiguation task. Task organizers provided monolingual data in several languages, but no cross-lingual training data were available. To address the lack of the officially provided cross-lingual training data, we decided to generate such data ourselves. We describe a simple yet effective approach based on machine translation and back translation of the lexical units to the original language used in the context of this shared task. In our experiments, we used a neural system based on the XLM-R, a pre-trained transformer-based masked language model, as a baseline. We show the effectiveness of the proposed approach as it allows to substantially improve the performance of this strong neural baseline model. In addition, in this study, we present multiple types of the XLM-R based classifier, experimenting with various ways of mixing information from the first and second occurrences of the target word in two samples.

pdf bib
SkoltechNLP at SemEval-2021 Task 5: Leveraging Sentence-level Pre-training for Toxic Span Detection
David Dale | Igor Markov | Varvara Logacheva | Olga Kozlova | Nikita Semenov | Alexander Panchenko
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This work describes the participation of the Skoltech NLP group team (Sk) in the Toxic Spans Detection task at SemEval-2021. The goal of the task is to identify the most toxic fragments of a given sentence, which is a binary sequence tagging problem. We show that fine-tuning a RoBERTa model for this problem is a strong baseline. This baseline can be further improved by pre-training the RoBERTa model on a large dataset labeled for toxicity at the sentence level. While our solution scored among the top 20% participating models, it is only 2 points below the best result. This suggests the viability of our approach.

pdf bib
Cross-lingual Evidence Improves Monolingual Fake News Detection
Daryna Dementieva | Alexander Panchenko
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop

Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases. Therefore, it is becoming essential to develop fake news detection technologies. While substantial work has been done in this direction, one of the limitations of the current approaches is that these models are focused only on one language and do not use multilingual information. In this work, we propose a new technique based on cross-lingual evidence (CE) that can be used for fake news detection and improve existing approaches. The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed, firstly, by manual experiment based on a set of known true and fake news. Besides, we compared our fake news classification system based on the proposed feature with several strong baselines on two multi-domain datasets of general-topic news and one newly fake COVID-19 news dataset showing that combining cross-lingual evidence with strong baselines such as RoBERTa yields significant improvements in fake news detection.

pdf bib
Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions
Irina Nikishina | Natalia Loukachevitch | Varvara Logacheva | Alexander Panchenko
Proceedings of the 11th Global Wordnet Conference

The vast majority of the existing approaches for taxonomy enrichment apply word embeddings as they have proven to accumulate contexts (in a broad sense) extracted from texts which are sufficient for attaching orphan words to the taxonomy. On the other hand, apart from being large lexical and semantic resources, taxonomies are graph structures. Combining word embeddings with graph structure of taxonomy could be of use for predicting taxonomic relations. In this paper we compare several approaches for attaching new words to the existing taxonomy which are based on the graph representations with the one that relies on fastText embeddings. We test all methods on Russian and English datasets, but they could be also applied to other wordnets and languages.

pdf bib
Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)
Alexander Panchenko | Fragkiskos D. Malliaros | Varvara Logacheva | Abhik Jana | Dmitry Ustalov | Peter Jansen
Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)

pdf bib
Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company’s Reputation
Nikolay Babakov | Varvara Logacheva | Olga Kozlova | Nikita Semenov | Alexander Panchenko
Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing

Not all topics are equally “flammable” in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.

2020

pdf bib
Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)
Dmitry Ustalov | Swapna Somasundaran | Alexander Panchenko | Fragkiskos D. Malliaros | Ioana Hulpuș | Peter Jansen | Abhik Jana
Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)

pdf bib
Generating Lexical Representations of Frames using Lexical Substitution
Saba Anwar | Artem Shelmanov | Alexander Panchenko | Chris Biemann
Proceedings of the Probability and Meaning Conference (PaM 2020)

Semantic frames are formal linguistic structures describing situations/actions/events, e.g. Commercial transfer of goods. Each frame provides a set of roles corresponding to the situation participants, e.g. Buyer and Goods, and lexical units (LUs) – words and phrases that can evoke this particular frame in texts, e.g. Sell. The scarcity of annotated resources hinders wider adoption of frame semantics across languages and domains. We investigate a simple yet effective method, lexical substitution with word representation models, to automatically expand a small set of frame-annotated sentences with new words for their respective roles and LUs. We evaluate the expansion quality using FrameNet. Contextualized models demonstrate overall superior performance compared to the non-contextualized ones on roles. However, the latter show comparable performance on the task of LU expansion.

pdf bib
SkoltechNLP at SemEval-2020 Task 11: Exploring Unsupervised Text Augmentation for Propaganda Detection
Daryna Dementieva | Igor Markov | Alexander Panchenko
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper presents a solution for the Span Identification (SI) task in the “Detection of Propaganda Techniques in News Articles” competition at SemEval-2020. The goal of the SI task is to identify specific fragments of each article which contain the use of at least one propaganda technique. This is a binary sequence tagging task. We tested several approaches finally selecting a fine-tuned BERT model as our baseline model. Our main contribution is an investigation of several unsupervised data augmentation techniques based on distributional semantics expanding the original small training dataset as applied to this BERT-based sequence tagger. We explore various expansion strategies and show that they can substantially shift the balance between precision and recall, while maintaining comparable levels of the F1 score.

pdf bib
Word Sense Disambiguation for 158 Languages using Word Embeddings Only
Varvara Logacheva | Denis Teslenko | Artem Shelmanov | Steffen Remus | Dmitry Ustalov | Andrey Kutuzov | Ekaterina Artemova | Chris Biemann | Simone Paolo Ponzetto | Alexander Panchenko
Proceedings of the Twelfth Language Resources and Evaluation Conference

Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian distribution of supervised training instances for a given word and/or (ii) the quality of linguistic knowledge representations motivate the development of completely unsupervised and knowledge-free approaches to word sense disambiguation (WSD). They are particularly useful for under-resourced languages which do not have any resources for building either supervised and/or knowledge-based models. In this paper, we present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory, which can be used for disambiguation in context. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave et al., (2018), enabling WSD in these languages. Models and system are available online.

pdf bib
Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP
Oren Sar Shalom | Alexander Panchenko | Cicero dos Santos | Varvara Logacheva | Alessandro Moschitti | Ido Dagan
Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP

pdf bib
Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution
Nikolay Arefyev | Boris Sheludko | Alexander Podolskiy | Alexander Panchenko
Proceedings of the 28th International Conference on Computational Linguistics

Lexical substitution, i.e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a backbone of various NLP applications, including word sense induction and disambiguation, lexical relation extraction, data augmentation, etc. In this paper, we present a large-scale comparative study of lexical substitution methods employing both rather old and most recent language and masked language models (LMs and MLMs), such as context2vec, ELMo, BERT, RoBERTa, XLNet. We show that already competitive results achieved by SOTA LMs/MLMs can be further substantially improved if information about the target word is injected properly. Several existing and new target word injection methods are compared for each LM/MLM using both intrinsic evaluation on lexical substitution datasets and extrinsic evaluation on word sense induction (WSI) datasets. On two WSI datasets we obtain new SOTA results. Besides, we analyze the types of semantic relations between target words and their substitutes generated by different models or given by annotators.

pdf bib
Studying Taxonomy Enrichment on Diachronic WordNet Versions
Irina Nikishina | Varvara Logacheva | Alexander Panchenko | Natalia Loukachevitch
Proceedings of the 28th International Conference on Computational Linguistics

Ontologies, taxonomies, and thesauri have always been in high demand in a large number of NLP tasks. However, most studies are focused on the creation of lexical resources rather than the maintenance of the existing ones and keeping them up-to-date. In this paper, we address the problem of taxonomy enrichment. Namely, we explore the possibilities of taxonomy extension in a resource-poor setting and present several methods which are applicable to a large number of languages. We also create novel English and Russian datasets for training and evaluating taxonomy enrichment systems and describe a technique of creating such datasets for other languages.

2019

pdf bib
Combining Lexical Substitutes in Neural Word Sense Induction
Nikolay Arefyev | Boris Sheludko | Alexander Panchenko
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Word Sense Induction (WSI) is the task of grouping of occurrences of an ambiguous word according to their meaning. In this work, we improve the approach to WSI proposed by Amrami and Goldberg (2018) based on clustering of lexical substitutes for an ambiguous word in a particular context obtained from neural language models. Namely, we propose methods for combining information from left and right context and similarity to the ambiguous word, which result in generating more accurate substitutes than the original approach. Our simple yet efficient improvement establishes a new state-of-the-art on WSI datasets for two languages. Besides, we show improvements to the original approach on a lexical substitution dataset.

pdf bib
On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings
Abhik Jana | Dima Puzyrev | Alexander Panchenko | Pawan Goyal | Chris Biemann | Animesh Mukherjee
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The compositionality degree of multiword expressions indicates to what extent the meaning of a phrase can be derived from the meaning of its constituents and their grammatical relations. Prediction of (non)-compositionality is a task that has been frequently addressed with distributional semantic models. We introduce a novel technique to blend hierarchical information with distributional information for predicting compositionality. In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincaré embeddings in addition to the distributional information to detect compositionality for noun phrases. Using a weighted average of the distributional similarity and a Poincaré similarity function, we obtain consistent and substantial, statistically significant improvement across three gold standard datasets over state-of-the-art models based on distributional information only. Unlike traditional approaches that solely use an unsupervised setting, we have also framed the problem as a supervised task, obtaining comparable improvements. Further, we publicly release our Poincaré embeddings, which are trained on the output of handcrafted lexical-syntactic patterns on a large corpus.

pdf bib
Making Fast Graph-based Algorithms with Graph Metric Embeddings
Andrey Kutuzov | Mohammad Dorgham | Oleksiy Oliynyk | Chris Biemann | Alexander Panchenko
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Graph measures, such as node distances, are inefficient to compute. We explore dense vector representations as an effective way to approximate the same information. We introduce a simple yet efficient and effective approach for learning graph embeddings. Instead of directly operating on the graph structure, our method takes structural measures of pairwise node similarities into account and learns dense node representations reflecting user-defined graph distance measures, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account. We demonstrate a speed-up of several orders of magnitude when predicting word similarity by vector operations on our embeddings as opposed to directly computing the respective path-based measures, while outperforming various other graph embeddings on semantic similarity and word sense disambiguation tasks.

pdf bib
Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings
Rami Aly | Shantanu Acharya | Alexander Ossa | Arne Köhn | Chris Biemann | Alexander Panchenko
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We introduce the use of Poincaré embeddings to improve existing state-of-the-art approaches to domain-specific taxonomy induction from text as a signal for both relocating wrong hyponym terms within a (pre-induced) taxonomy as well as for attaching disconnected terms in a taxonomy. This method substantially improves previous state-of-the-art results on the SemEval-2016 Task 13 on taxonomy extraction. We demonstrate the superiority of Poincaré embeddings over distributional semantic representations, supporting the hypothesis that they can better capture hierarchical lexical-semantic relationships than embeddings in the Euclidean space.

pdf bib
Improving Neural Entity Disambiguation with Graph Embeddings
Özge Sevgili | Alexander Panchenko | Chris Biemann
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base. Current methods have mostly focused on unstructured text data to learn representations of entities, however, there is structured information in the knowledge base itself that should be useful to disambiguate entities. In this work, we propose a method that uses graph embeddings for integrating structured information from the knowledge base with unstructured information from text-based representations. Our experiments confirm that graph embeddings trained on a graph of hyperlinks between Wikipedia articles improve the performances of simple feed-forward neural ED model and a state-of-the-art neural ED system.

pdf bib
TARGER: Neural Argument Mining at Your Fingertips
Artem Chernodub | Oleksiy Oliynyk | Philipp Heidenreich | Alexander Bondarenko | Matthias Hagen | Chris Biemann | Alexander Panchenko
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We present TARGER, an open source neural argument mining framework for tagging arguments in free input texts and for keyword-based retrieval of arguments from an argument-tagged web-scale corpus. The currently available models are pre-trained on three recent argument mining datasets and enable the use of neural argument mining without any reproducibility effort on the user’s side. The open source code ensures portability to other domains and use cases.

pdf bib
Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction
Dmitry Ustalov | Alexander Panchenko | Chris Biemann | Simone Paolo Ponzetto
Computational Linguistics, Volume 45, Issue 3 - September 2019

We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains. This algorithm creates an intermediate representation of the input graph, which reflects the “ambiguity” of its nodes. Then, it uses hard clustering to discover clusters in this “disambiguated” intermediate graph. After outlining the approach and analyzing its computational complexity, we demonstrate that Watset shows competitive results in three applications: unsupervised synset induction from a synonymy graph, unsupervised semantic frame induction from dependency triples, and unsupervised semantic class induction from a distributional thesaurus. Our algorithm is generic and can also be applied to other networks of linguistic data.

pdf bib
Learning Graph Embeddings from WordNet-based Similarity Measures
Andrey Kutuzov | Mohammad Dorgham | Oleksiy Oliynyk | Chris Biemann | Alexander Panchenko
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

We present path2vec, a new approach for learning graph embeddings that relies on structural measures of pairwise node similarities. The model learns representations for nodes in a dense space that approximate a given user-defined graph distance measure, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account. Evaluation of the proposed model on semantic similarity and word sense disambiguation tasks, using various WordNet-based similarity measures, show that our approach yields competitive results, outperforming strong graph embedding baselines. The model is computationally efficient, being orders of magnitude faster than the direct computation of graph-based distances.

pdf bib
Neural GRANNy at SemEval-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction
Nikolay Arefyev | Boris Sheludko | Adis Davletov | Dmitry Kharchev | Alex Nevidomsky | Alexander Panchenko
Proceedings of the 13th International Workshop on Semantic Evaluation

We describe our solutions for semantic frame and role induction subtasks of SemEval 2019 Task 2. Our approaches got the highest scores, and the solution for the frame induction problem officially took the first place. The main contributions of this paper are related to the semantic frame induction problem. We propose a combined approach that employs two different types of vector representations: dense representations from hidden layers of a masked language model, and sparse representations based on substitutes for the target word in the context. The first one better groups synonyms, the second one is better at disambiguating homonyms. Extending the context to include nearby sentences improves the results in both cases. New Hearst-like patterns for verbs are introduced that prove to be effective for frame induction. Finally, we propose an approach to selecting the number of clusters in agglomerative clustering.

pdf bib
HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings
Saba Anwar | Dmitry Ustalov | Nikolay Arefyev | Simone Paolo Ponzetto | Chris Biemann | Alexander Panchenko
Proceedings of the 13th International Workshop on Semantic Evaluation

We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (Qasem-iZadeh et al., 2019). Our approach separates this task into two independent steps: verb clustering using word and their context embeddings and role labeling by combining these embeddings with syntactical features. A simple combination of these steps shows very competitive results and can be extended to process other datasets and languages.

pdf bib
A Dataset for Noun Compositionality Detection for a Slavic Language
Dmitry Puzyrev | Artem Shelmanov | Alexander Panchenko | Ekaterina Artemova
Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

This paper presents the first gold-standard resource for Russian annotated with compositionality information of noun compounds. The compound phrases are collected from the Universal Dependency treebanks according to part of speech patterns, such as ADJ+NOUN or NOUN+NOUN, using the gold-standard annotations. Each compound phrase is annotated by two experts and a moderator according to the following schema: the phrase can be either compositional, non-compositional, or ambiguous (i.e., depending on the context it can be interpreted both as compositional or non-compositional). We conduct an experimental evaluation of models and methods for predicting compositionality of noun compounds in unsupervised and supervised setups. We show that methods from previous work evaluated on the proposed Russian-language resource achieve the performance comparable with results on English corpora.

pdf bib
Categorizing Comparative Sentences
Alexander Panchenko | Alexander Bondarenko | Mirco Franzek | Matthias Hagen | Chris Biemann
Proceedings of the 6th Workshop on Argument Mining

We tackle the tasks of automatically identifying comparative sentences and categorizing the intended preference (e.g., “Python has better NLP libraries than MATLAB” → Python, better, MATLAB). To this end, we manually annotate 7,199 sentences for 217 distinct target item pairs from several domains (27% of the sentences contain an oriented comparison in the sense of “better” or “worse”). A gradient boosting model based on pre-trained sentence embeddings reaches an F1 score of 85% in our experimental evaluation. The model can be used to extract comparative sentences for pro/con argumentation in comparative / argument search engines or debating technologies.

2018

pdf bib
Enriching Frame Representations with Distributionally Induced Senses
Stefano Faralli | Alexander Panchenko | Chris Biemann | Simone Paolo Ponzetto
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages
Dmitry Ustalov | Denis Teslenko | Alexander Panchenko | Mikhail Chernoskutov | Chris Biemann | Simone Paolo Ponzetto
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
Improving Hypernymy Extraction with Distributional Semantic Classes
Alexander Panchenko | Dmitry Ustalov | Stefano Faralli | Simone P. Ponzetto | Chris Biemann
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl
Alexander Panchenko | Eugen Ruppert | Stefano Faralli | Simone P. Ponzetto | Chris Biemann
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
Unsupervised Semantic Frame Induction using Triclustering
Dmitry Ustalov | Alexander Panchenko | Andrey Kutuzov | Chris Biemann | Simone Paolo Ponzetto
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We use dependency triples automatically extracted from a Web-scale corpus to perform unsupervised semantic frame induction. We cast the frame induction problem as a triclustering problem that is a generalization of clustering for triadic data. Our replicable benchmarks demonstrate that the proposed graph-based approach, Triframes, shows state-of-the art results on this task on a FrameNet-derived dataset and performing on par with competitive methods on a verb class clustering task.

2017

pdf bib
Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation
Alexander Panchenko | Stefano Faralli | Simone Paolo Ponzetto | Chris Biemann
Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications

We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on a resource that links two types of sense-aware lexical networks: one is induced from a corpus using distributional semantics, the other is manually constructed. The combination of two networks reduces the sparsity of sense representations used for WSD. We evaluate these enriched representations within two lexical sample sense disambiguation benchmarks. Our results indicate that (1) features extracted from the corpus-based resource help to significantly outperform a model based solely on the lexical resource; (2) our method achieves results comparable or better to four state-of-the-art unsupervised knowledge-based WSD systems including three hybrid systems that also rely on text corpora. In contrast to these hybrid methods, our approach does not require access to web search engines, texts mapped to a sense inventory, or machine translation systems.

pdf bib
Entity-Centric Information Access with Human in the Loop for the Biomedical Domain
Seid Muhie Yimam | Steffen Remus | Alexander Panchenko | Andreas Holzinger | Chris Biemann
Proceedings of the Biomedical NLP Workshop associated with RANLP 2017

In this paper, we describe the concept of entity-centric information access for the biomedical domain. With entity recognition technologies approaching acceptable levels of accuracy, we put forward a paradigm of document browsing and searching where the entities of the domain and their relations are explicitly modeled to provide users the possibility of collecting exhaustive information on relations of interest. We describe three working prototypes along these lines: NEW/S/LEAK, which was developed for investigative journalists who need a quick overview of large leaked document collections; STORYFINDER, which is a personalized organizer for information found in web pages that allows adding entities as well as relations, and is capable of personalized information management; and adaptive annotation capabilities of WEBANNO, which is a general-purpose linguistic annotation tool. We will discuss future steps towards the adaptation of these tools to biomedical data, which is subject to a recently started project on biomedical knowledge acquisition. A key difference to other approaches is the centering around the user in a Human-in-the-Loop machine learning approach, where users define and extend categories and enable the system to improve via feedback and interaction.

pdf bib
Watset: Automatic Induction of Synsets from a Graph of Synonyms
Dmitry Ustalov | Alexander Panchenko | Chris Biemann
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the disambiguated version of the ambiguous input graph into synsets. Our meta-clustering approach lets us use an efficient hard clustering algorithm to perform a fuzzy clustering of the graph. Despite its simplicity, our approach shows excellent results, outperforming five competitive state-of-the-art methods in terms of F-score on three gold standard datasets for English and Russian derived from large-scale manually constructed lexical resources.

pdf bib
Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
Alexander Panchenko | Fide Marten | Eugen Ruppert | Stefano Faralli | Dmitry Ustalov | Simone Paolo Ponzetto | Chris Biemann
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images. We present a WSD system that bridges the gap between these two so far disconnected groups of methods. Namely, our system, providing access to several state-of-the-art WSD models, aims to be interpretable as a knowledge-based system while it remains completely unsupervised and knowledge-free. The presented tool features a Web interface for all-word disambiguation of texts that makes the sense predictions human readable by providing interpretable word sense inventories, sense representations, and disambiguation results. We provide a public API, enabling seamless integration.

pdf bib
Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation
Alexander Panchenko | Eugen Ruppert | Stefano Faralli | Simone Paolo Ponzetto | Chris Biemann
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

The current trend in NLP is the use of highly opaque models, e.g. neural networks and word embeddings. While these models yield state-of-the-art results on a range of tasks, their drawback is poor interpretability. On the example of word sense induction and disambiguation (WSID), we show that it is possible to develop an interpretable model that matches the state-of-the-art models in accuracy. Namely, we present an unsupervised, knowledge-free WSID approach, which is interpretable at three levels: word sense inventory, sense feature representations, and disambiguation procedure. Experiments show that our model performs on par with state-of-the-art word sense embeddings and other unsupervised systems while offering the possibility to justify its decisions in human-readable form.

pdf bib
The ContrastMedium Algorithm: Taxonomy Induction From Noisy Knowledge Graphs With Just A Few Links
Stefano Faralli | Alexander Panchenko | Chris Biemann | Simone Paolo Ponzetto
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

In this paper, we present ContrastMedium, an algorithm that transforms noisy semantic networks into full-fledged, clean taxonomies. ContrastMedium is able to identify the embedded taxonomy structure from a noisy knowledge graph without explicit human supervision such as, for instance, a set of manually selected input root and leaf concepts. This is achieved by leveraging structural information from a companion reference taxonomy, to which the input knowledge graph is linked (either automatically or manually). When used in conjunction with methods for hypernym acquisition and knowledge base linking, our methodology provides a complete solution for end-to-end taxonomy induction. We conduct experiments using automatically acquired knowledge graphs, as well as a SemEval benchmark, and show that our method is able to achieve high performance on the task of taxonomy induction.

pdf bib
Negative Sampling Improves Hypernymy Extraction Based on Projection Learning
Dmitry Ustalov | Nikolay Arefyev | Chris Biemann | Alexander Panchenko
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of positive examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach of Fu et al. (2014) on three datasets from different languages.

2016

pdf bib
Best of Both Worlds: Making Word Sense Embeddings Interpretable
Alexander Panchenko
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Word sense embeddings represent a word sense as a low-dimensional numeric vector. While this representation is potentially useful for NLP applications, its interpretability is inherently limited. We propose a simple technique that improves interpretability of sense vectors by mapping them to synsets of a lexical resource. Our experiments with AdaGram sense embeddings and BabelNet synsets show that it is possible to retrieve synsets that correspond to automatically learned sense vectors with Precision of 0.87, Recall of 0.42 and AUC of 0.78.

pdf bib
Making Sense of Word Embeddings
Maria Pelevina | Nikolay Arefiev | Chris Biemann | Alexander Panchenko
Proceedings of the 1st Workshop on Representation Learning for NLP

pdf bib
TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling
Alexander Panchenko | Stefano Faralli | Eugen Ruppert | Steffen Remus | Hubert Naets | Cédrick Fairon | Simone Paolo Ponzetto | Chris Biemann
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

pdf bib
new/s/leak – Information Extraction and Visualization for Investigative Data Journalists
Seid Muhie Yimam | Heiner Ulrich | Tatiana von Landesberger | Marcel Rosenbach | Michaela Regneri | Alexander Panchenko | Franziska Lehmann | Uli Fahrer | Chris Biemann | Kathrin Ballweg
Proceedings of ACL-2016 System Demonstrations

2013

pdf bib
Search and Visualization of Semantically Related Words (Recherche et visualisation de mots sémantiquement liés) [in French]
Alexander Panchenko | Hubert Naets | Laetitia Brouwers | Pavel Romanov | Cédrick Fairon
Proceedings of TALN 2013 (Volume 2: Short Papers)

pdf bib
A Graph-Based Approach to Skill Extraction from Text
Ilkka Kivimäki | Alexander Panchenko | Adrien Dessy | Dries Verdegem | Pascal Francq | Hugues Bersini | Marco Saerens
Proceedings of TextGraphs-8 Graph-based Methods for Natural Language Processing

2012

pdf bib
A Study of Hybrid Similarity Measures for Semantic Relation Extraction
Alexander Panchenko | Olga Morozova
Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data

pdf bib
A Study of Heterogeneous Similarity Measures for Semantic Relation Extraction
Alexander Panchenko
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 3: RECITAL

2011

pdf bib
Comparison of the Baseline Knowledge-, Corpus-, and Web-based Similarity Measures for Semantic Relations Extraction
Alexander Panchenko
Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics

Search
Co-authors