Cornelia Caragea


2023

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Semi-Supervised Domain Adaptation for Emotion-Related Tasks
Mahshid Hosseini | Cornelia Caragea
Findings of the Association for Computational Linguistics: ACL 2023

Semi-supervised domain adaptation (SSDA) adopts a model trained from a label-rich source domain to a new but related domain with a few labels of target data. It is shown that, in an SSDA setting, a simple combination of domain adaptation (DA) with semi-supervised learning (SSL) techniques often fails to effectively utilize the target supervision and cannot address distribution shifts across different domains due to the training data bias toward the source-labeled samples. In this paper, inspired by the co-learning of multiple classifiers for the computer vision tasks, we propose to decompose the SSDA framework for emotion-related tasks into two subcomponents of unsupervised domain adaptation (UDA) from the source to the target domain and semi-supervised learning (SSL) in the target domain where the two models iteratively teach each other by interchanging their high confident predictions. We further propose a novel data cartography-based regularization technique for pseudo-label denoising that employs training dynamics to further hone our models’ performance. We publicly release our code.

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Distilling Calibrated Knowledge for Stance Detection
Yingjie Li | Cornelia Caragea
Findings of the Association for Computational Linguistics: ACL 2023

Stance detection aims to determine the position of an author toward a target and provides insights into people’s views on controversial topics such as marijuana legalization. Despite recent progress in this task, most existing approaches use hard labels (one-hot vectors) during training, which ignores meaningful signals among categories offered by soft labels. In this work, we explore knowledge distillation for stance detection and present a comprehensive analysis. Our contributions are: 1) we propose to use knowledge distillation over multiple generations in which a student is taken as a new teacher to transfer knowledge to a new fresh student; 2) we propose a novel dynamic temperature scaling for knowledge distillation to calibrate teacher predictions in each generation step. Extensive results on three stance detection datasets show that knowledge distillation benefits stance detection and a teacher is able to transfer knowledge to a student more smoothly via calibrated guiding signals. We publicly release our code to facilitate future research.

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Data Augmentation for Low-Resource Keyphrase Generation
Krishna Garg | Jishnu Ray Chowdhury | Cornelia Caragea
Findings of the Association for Computational Linguistics: ACL 2023

Keyphrase generation is the task of summarizing the contents of any given article into a few salient phrases (or keyphrases). Existing works for the task mostly rely on large-scale annotated datasets, which are not easy to acquire. Very few works address the problem of keyphrase generation in low-resource settings, but they still rely on a lot of additional unlabeled data for pretraining and on automatic methods for pseudo-annotations. In this paper, we present data augmentation strategies specifically to address keyphrase generation in purely resource-constrained domains. We design techniques that use the full text of the articles to improve both present and absent keyphrase generation. We test our approach comprehensively on three datasets and show that the data augmentation strategies consistently improve the state-of-the-art performance. We release our source code at https://github.com/kgarg8/kpgen-lowres-data-aug.

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DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank
Henry Zou | Yue Zhou | Weizhi Zhang | Cornelia Caragea
Findings of the Association for Computational Linguistics: EMNLP 2023

During crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support. Emergency relief organizations leverage such information to acquire timely crisis circumstances and expedite rescue operations. While existing works utilize such information to build models for crisis event analysis, fully-supervised approaches require annotating vast amounts of data and are impractical due to limited response time. On the other hand, semi-supervised models can be biased, performing moderately well for certain classes while performing extremely poorly for others, resulting in substantially negative effects on disaster monitoring and rescue. In this paper, we first study two recent debiasing methods on semi-supervised crisis tweet classification. Then we propose a simple but effective debiasing method, DeCrisisMB, that utilizes a Memory Bank to store and perform equal sampling for generated pseudo-labels from each class at each training iteration. Extensive experiments are conducted to compare different debiasing methods’ performance and generalization ability in both in-distribution and out-of-distribution settings. The results demonstrate the superior performance of our proposed method. Our code is available at https://github.com/HenryPengZou/DeCrisisMB.

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JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification
Henry Zou | Cornelia Caragea
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Semi-supervised text classification (SSTC) has gained increasing attention due to its ability to leverage unlabeled data. However, existing approaches based on pseudo-labeling suffer from the issues of pseudo-label bias and error accumulation. In this paper, we propose JointMatch, a holistic approach for SSTC that addresses these challenges by unifying ideas from recent semi-supervised learning and the task of learning with noise. JointMatch adaptively adjusts classwise thresholds based on the learning status of different classes to mitigate model bias towards current easy classes. Additionally, JointMatch alleviates error accumulation by utilizing two differently initialized networks to teach each other in a cross-labeling manner. To maintain divergence between the two networks for mutual learning, we introduce a strategy that weighs more disagreement data while also allowing the utilization of high-quality agreement data for training. Experimental results on benchmark datasets demonstrate the superior performance of JointMatch, achieving a significant 5.13% improvement on average. Notably, JointMatch delivers impressive results even in the extremely-scarce-label setting, obtaining 86% accuracy on AG News with only 5 labels per class. We make our code available at https://github.com/HenryPengZou/JointMatch.

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Multi-Task Knowledge Distillation with Embedding Constraints for Scholarly Keyphrase Boundary Classification
Seo Park | Cornelia Caragea
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

The task of scholarly keyphrase boundary classification aims at identifying keyphrases from scientific papers and classifying them with their types from a set of predefined classes (e.g., task, process, or material). Despite the importance of keyphrases and their types in many downstream applications including indexing, searching, and question answering over scientific documents, scholarly keyphrase boundary classification is still an under-explored task. In this work, we propose a novel embedding constraint on multi-task knowledge distillation which enforces the teachers (single-task models) and the student (multi-task model) similarity in the embedding space. Specifically, we enforce that the student model is trained not only to imitate the teachers’ output distribution over classes, but also to produce language representations that are similar to those produced by the teachers. Our results show that the proposed approach outperforms previous works and strong baselines on three datasets of scientific documents.

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What Clued the AI Doctor In? On the Influence of Data Source and Quality for Transformer-Based Medical Self-Disclosure Detection
Mina Valizadeh | Xing Qian | Pardis Ranjbar-Noiey | Cornelia Caragea | Natalie Parde
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Recognizing medical self-disclosure is important in many healthcare contexts, but it has been under-explored by the NLP community. We conduct a three-pronged investigation of this task. We (1) manually expand and refine the only existing medical self-disclosure corpus, resulting in a new, publicly available dataset of 3,919 social media posts with clinically validated labels and high compatibility with the existing task-specific protocol. We also (2) study the merits of pretraining task domain and text style by comparing Transformer-based models for this task, pretrained from general, medical, and social media sources. Our BERTweet condition outperforms the existing state of the art for this task by a relative F1 score increase of 16.73%. Finally, we (3) compare data augmentation techniques for this task, to assess the extent to which medical self-disclosure data may be further synthetically expanded. We discover that this task poses many challenges for data augmentation techniques, and we provide an in-depth analysis of identified trends.

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DeepZensols: A Deep Learning Natural Language Processing Framework for Experimentation and Reproducibility
Paul Landes | Barbara Di Eugenio | Cornelia Caragea
Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)

Given the criticality and difficulty of reproducing machine learning experiments, there have been significant efforts in reducing the variance of these results. The ability to consistently reproduce results effectively strengthens the underlying hypothesis of the work and should be regarded as important as the novel aspect of the research itself. The contribution of this work is an open source framework that has the following characteristics: a) facilitates reproducing consistent results, b) allows hot-swapping features and embeddings without further processing and re-vectorizing the dataset, c) provides a means of easily creating, training and evaluating natural language processing deep learning models with little to no code changes, and d) is freely available to the community.

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DMDD: A Large-Scale Dataset for Dataset Mentions Detection
Huitong Pan | Qi Zhang | Eduard Dragut | Cornelia Caragea | Longin Jan Latecki
Transactions of the Association for Computational Linguistics, Volume 11

The recognition of dataset names is a critical task for automatic information extraction in scientific literature, enabling researchers to understand and identify research opportunities. However, existing corpora for dataset mention detection are limited in size and naming diversity. In this paper, we introduce the Dataset Mentions Detection Dataset (DMDD), the largest publicly available corpus for this task. DMDD consists of the DMDD main corpus, comprising 31,219 scientific articles with over 449,000 dataset mentions weakly annotated in the format of in-text spans, and an evaluation set, which comprises 450 scientific articles manually annotated for evaluation purposes. We use DMDD to establish baseline performance for dataset mention detection and linking. By analyzing the performance of various models on DMDD, we are able to identify open problems in dataset mention detection. We invite the community to use our dataset as a challenge to develop novel dataset mention detection models.

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Towards Identifying Fine-Grained Depression Symptoms from Memes
Shweta Yadav | Cornelia Caragea | Chenye Zhao | Naincy Kumari | Marvin Solberg | Tanmay Sharma
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The past decade has observed significant attention toward developing computational methods for classifying social media data based on the presence or absence of mental health conditions. In the context of mental health, for clinicians to make an accurate diagnosis or provide personalized intervention, it is crucial to identify fine-grained mental health symptoms. To this end, we conduct a focused study on depression disorder and introduce a new task of identifying fine-grained depressive symptoms from memes. Toward this, we create a high-quality dataset (RESTORE) annotated with 8 fine-grained depression symptoms based on the clinically adopted PHQ-9 questionnaire. We benchmark RESTORE on 20 strong monomodal and multimodal methods. Additionally, we show how imposing orthogonal constraints on textual and visual feature representations in a multimodal setting can enforce the model to learn non-redundant and de-correlated features leading to a better prediction of fine-grained depression symptoms. Further, we conduct an extensive human analysis and elaborate on the limitations of existing multimodal models that often overlook the implicit connection between visual and textual elements of a meme.

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Unsupervised Extractive Summarization of Emotion Triggers
Tiberiu Sosea | Hongli Zhan | Junyi Jessy Li | Cornelia Caragea
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Understanding what leads to emotions during large-scale crises is important as it can provide groundings for expressed emotions and subsequently improve the understanding of ongoing disasters. Recent approaches trained supervised models to both detect emotions and explain emotion triggers (events and appraisals) via abstractive summarization. However, obtaining timely and qualitative abstractive summaries is expensive and extremely time-consuming, requiring highly-trained expert annotators. In time-sensitive, high-stake contexts, this can block necessary responses. We instead pursue unsupervised systems that extract triggers from text. First, we introduce CovidET-EXT, augmenting (Zhan et al., 2022)’s abstractive dataset (in the context of the COVID-19 crisis) with extractive triggers. Second, we develop new unsupervised learning models that can jointly detect emotions and summarize their triggers. Our best approach, entitled Emotion-Aware Pagerank, incorporates emotion information from external sources combined with a language understanding module, and outperforms strong baselines. We release our data and code at https://github.com/tsosea2/CovidET-EXT.

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A New Direction in Stance Detection: Target-Stance Extraction in the Wild
Yingjie Li | Krishna Garg | Cornelia Caragea
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Stance detection aims to detect the stance toward a corresponding target. Existing works use the assumption that the target is known in advance, which is often not the case in the wild. Given a text from social media platforms, the target information is often unknown due to implicit mentions in the source text and it is infeasible to have manual target annotations at a large scale. Therefore, in this paper, we propose a new task Target-Stance Extraction (TSE) that aims to extract the (target, stance) pair from the text. We benchmark the task by proposing a two-stage framework that first identifies the relevant target in the text and then detects the stance given the predicted target and text. Specifically, we first propose two different settings: Target Classification and Target Generation, to identify the potential target from a given text. Then we propose a multi-task approach that takes target prediction as the auxiliary task to detect the stance toward the predicted target. We evaluate the proposed framework on both in-target stance detection in which the test target is always seen in the training stage and zero-shot stance detection that needs to detect the stance for the targets that are unseen during the training phase. The new TSE task can facilitate future research in the field of stance detection.

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C-STANCE: A Large Dataset for Chinese Zero-Shot Stance Detection
Chenye Zhao | Yingjie Li | Cornelia Caragea
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Zero-shot stance detection (ZSSD) aims to determine whether the author of a text is in favor of, against, or neutral toward a target that is unseen during training. Despite the growing attention on ZSSD, most recent advances in this task are limited to English and do not pay much attention to other languages such as Chinese. To support ZSSD research, in this paper, we present C-STANCE that, to our knowledge, is the first Chinese dataset for zero-shot stance detection. We introduce two challenging subtasks for ZSSD: target-based ZSSD and domain-based ZSSD. Our dataset includes both noun-phrase targets and claim targets, covering a wide range of domains. We provide a detailed description and analysis of our dataset. To establish results on C-STANCE, we report performance scores using state-of-the-art deep learning models. We publicly release our dataset and code to facilitate future research.

2022

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Leveraging Training Dynamics and Self-Training for Text Classification
Tiberiu Sosea | Cornelia Caragea
Findings of the Association for Computational Linguistics: EMNLP 2022

The effectiveness of pre-trained language models in downstream tasks is highly dependent on the amount of labeled data available for training. Semi-supervised learning (SSL) is a promising technique that has seen wide attention recently due to its effectiveness in improving deep learning models when training data is scarce. Common approaches employ a teacher-student self-training framework, where a teacher network generates pseudo-labels for unlabeled data, which are then used to iteratively train a student network. In this paper, we propose a new self-training approach for text classification that leverages training dynamics of unlabeled data. We evaluate our approach on a wide range of text classification tasks, including emotion detection, sentiment analysis, question classification and gramaticality, which span a variety of domains, e.g, Reddit, Twitter, and online forums. Notably, our method is successful on all benchmarks, obtaining an average increase in F1 score of 3.5% over strong baselines in low resource settings.

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Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference
Mobashir Sadat | Cornelia Caragea
Findings of the Association for Computational Linguistics: EMNLP 2022

Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) aims at predicting the relation between a pair of sentences (premise and hypothesis) as entailment, contradiction or semantic independence. Although deep learning models have shown promising performance for NLI in recent years, they rely on large scale expensive human-annotated datasets. Semi-supervised learning (SSL) is a popular technique for reducing the reliance on human annotation by leveraging unlabeled data for training. However, despite its substantial success on single sentence classification tasks where the challenge in making use of unlabeled data is to assign “good enough” pseudo-labels, for NLI tasks, the nature of unlabeled data is more complex: one of the sentences in the pair (usually the hypothesis) along with the class label are missing from the data and require human annotations, which makes SSL for NLI more challenging. In this paper, we propose a novel way to incorporate unlabeled data in SSL for NLI where we use a conditional language model, BART to generate the hypotheses for the unlabeled sentences (used as premises). Our experiments show that our SSL framework successfully exploits unlabeled data and substantially improves the performance of four NLI datasets in low-resource settings. We release our code here: https://github.com/msadat3/SSL_for_NLI

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KPDROP: Improving Absent Keyphrase Generation
Jishnu Ray Chowdhury | Seo Yeon Park | Tuhin Kundu | Cornelia Caragea
Findings of the Association for Computational Linguistics: EMNLP 2022

Keyphrase generation is the task of generating phrases (keyphrases) that summarize the main topics of a given document. Keyphrases can be either present or absent from the given document. While the extraction of present keyphrases has received much attention in the past, only recently a stronger focus has been placed on the generation of absent keyphrases. However, generating absent keyphrases is challenging; even the best methods show only a modest degree of success. In this paper, we propose a model-agnostic approach called keyphrase dropout (or KPDrop) to improve absent keyphrase generation. In this approach, we randomly drop present keyphrases from the document and turn them into artificial absent keyphrases during training. We test our approach extensively and show that it consistently improves the absent performance of strong baselines in both supervised and resource-constrained semi-supervised settings.

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Keyphrase Generation Beyond the Boundaries of Title and Abstract
Krishna Garg | Jishnu Ray Chowdhury | Cornelia Caragea
Findings of the Association for Computational Linguistics: EMNLP 2022

Keyphrase generation aims at generating important phrases (keyphrases) that best describe a given document. In scholarly domains, current approaches have largely used only the title and abstract of the articles to generate keyphrases. In this paper, we comprehensively explore whether the integration of additional information from the full text of a given article or from semantically similar articles can be helpful for a neural keyphrase generation model or not. We discover that adding sentences from the full text, particularly in the form of the extractive summary of the article can significantly improve the generation of both types of keyphrases that are either present or absent from the text. Experimental results with three widely used models for keyphrase generation along with one of the latest transformer models suitable for longer documents, Longformer Encoder-Decoder (LED) validate the observation. We also present a new large-scale scholarly dataset FullTextKP for keyphrase generation. Unlike prior large-scale datasets, FullTextKP includes the full text of the articles along with the title and abstract. We release the source code at https://github.com/kgarg8/FullTextKP.

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Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism
Ștefan Cobeli | Ioan-Bogdan Iordache | Shweta Yadav | Cornelia Caragea | Liviu P. Dinu | Dragoș Iliescu
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Finding the polarity of feelings in texts is a far-reaching task. Whilst the field of natural language processing has established sentiment analysis as an alluring problem, many feelings are left uncharted. In this study, we analyze the optimism and pessimism concepts from Twitter posts to effectively understand the broader dimension of psychological phenomenon. Towards this, we carried a systematic study by first exploring the linguistic peculiarities of optimism and pessimism in user-generated content. Later, we devised a multi-task knowledge distillation framework to simultaneously learn the target task of optimism detection with the help of the auxiliary task of sentiment analysis and hate speech detection. We evaluated the performance of our proposed approach on the benchmark Optimism/Pessimism Twitter dataset. Our extensive experiments show the superior- ity of our approach in correctly differentiating between optimistic and pessimistic users. Our human and automatic evaluation shows that sentiment analysis and hate speech detection are beneficial for optimism/pessimism detection.

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EnsyNet: A Dataset for Encouragement and Sympathy Detection
Tiberiu Sosea | Cornelia Caragea
Proceedings of the Thirteenth Language Resources and Evaluation Conference

More and more people turn to Online Health Communities to seek social support during their illnesses. By interacting with peers with similar medical conditions, users feel emotionally and socially supported, which in turn leads to better adherence to therapy. Current studies in Online Health Communities focus only on the presence or absence of emotional support, while the available datasets are scarce or limited in terms of size. To enable development on emotional support detection, we introduce EnsyNet, a dataset of 6,500 sentences annotated with two types of support: encouragement and sympathy. We train BERT-based classifiers on this dataset, and apply our best BERT model in two large scale experiments. The results of these experiments show that receiving encouragements or sympathy improves users’ emotional state, while the lack of emotional support negatively impacts patients’ emotional state.

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Emotion analysis and detection during COVID-19
Tiberiu Sosea | Chau Pham | Alexander Tekle | Cornelia Caragea | Junyi Jessy Li
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Understanding emotions that people express during large-scale crises helps inform policy makers and first responders about the emotional states of the population as well as provide emotional support to those who need such support. We present CovidEmo, a dataset of ~3,000 English tweets labeled with emotions and temporally distributed across 18 months. Our analyses reveal the emotional toll caused by COVID-19, and changes of the social narrative and associated emotions over time. Motivated by the time-sensitive nature of crises and the cost of large-scale annotation efforts, we examine how well large pre-trained language models generalize across domains and timeline in the task of perceived emotion prediction in the context of COVID-19. Our analyses suggest that cross-domain information transfers occur, yet there are still significant gaps. We propose semi-supervised learning as a way to bridge this gap, obtaining significantly better performance using unlabeled data from the target domain.

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On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency
Seo Yeon Park | Cornelia Caragea
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

A well-calibrated neural model produces confidence (probability outputs) closely approximated by the expected accuracy. While prior studies have shown that mixup training as a data augmentation technique can improve model calibration on image classification tasks, little is known about using mixup for model calibration on natural language understanding (NLU) tasks. In this paper, we explore mixup for model calibration on several NLU tasks and propose a novel mixup strategy for pre-trained language models that improves model calibration further. Our proposed mixup is guided by both the Area Under the Margin (AUM) statistic (Pleiss et al., 2020) and the saliency map of each sample (Simonyan et al., 2013). Moreover, we combine our mixup strategy with model miscalibration correction techniques (i.e., label smoothing and temperature scaling) and provide detailed analyses of their impact on our proposed mixup. We focus on systematically designing experiments on three NLU tasks: natural language inference, paraphrase detection, and commonsense reasoning. Our method achieves the lowest expected calibration error compared to strong baselines on both in-domain and out-of-domain test samples while maintaining competitive accuracy.

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SciNLI: A Corpus for Natural Language Inference on Scientific Text
Mobashir Sadat | Cornelia Caragea
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Existing Natural Language Inference (NLI) datasets, while being instrumental in the advancement of Natural Language Understanding (NLU) research, are not related to scientific text. In this paper, we introduce SciNLI, a large dataset for NLI that captures the formality in scientific text and contains 107,412 sentence pairs extracted from scholarly papers on NLP and computational linguistics. Given that the text used in scientific literature differs vastly from the text used in everyday language both in terms of vocabulary and sentence structure, our dataset is well suited to serve as a benchmark for the evaluation of scientific NLU models. Our experiments show that SciNLI is harder to classify than the existing NLI datasets. Our best performing model with XLNet achieves a Macro F1 score of only 78.18% and an accuracy of 78.23% showing that there is substantial room for improvement.

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Multimodal Semi-supervised Learning for Disaster Tweet Classification
Iustin Sirbu | Tiberiu Sosea | Cornelia Caragea | Doina Caragea | Traian Rebedea
Proceedings of the 29th International Conference on Computational Linguistics

During natural disasters, people often use social media platforms, such as Twitter, to post information about casualties and damage produced by disasters. This information can help relief authorities gain situational awareness in nearly real time, and enable them to quickly distribute resources where most needed. However, annotating data for this purpose can be burdensome, subjective and expensive. In this paper, we investigate how to leverage the copious amounts of unlabeled data generated on social media by disaster eyewitnesses and affected individuals during disaster events. To this end, we propose a semi-supervised learning approach to improve the performance of neural models on several multimodal disaster tweet classification tasks. Our approach shows significant improvements, obtaining up to 7.7% improvements in F-1 in low-data regimes and 1.9% when using the entire training data. We make our code and data publicly available at https://github.com/iustinsirbu13/multimodal-ssl-for-disaster-tweet-classification.

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Towards Summarizing Healthcare Questions in Low-Resource Setting
Shweta Yadav | Cornelia Caragea
Proceedings of the 29th International Conference on Computational Linguistics

The current advancement in abstractive document summarization depends to a large extent on a considerable amount of human-annotated datasets. However, the creation of large-scale datasets is often not feasible in closed domains, such as medical and healthcare domains, where human annotation requires domain expertise. This paper presents a novel data selection strategy to generate diverse and semantic questions in a low-resource setting with the aim to summarize healthcare questions. Our method exploits the concept of guided semantic-overlap and diversity-based objective functions to optimally select the informative and diverse set of synthetic samples for data augmentation. Our extensive experiments on benchmark healthcare question summarization datasets demonstrate the effectiveness of our proposed data selection strategy by achieving new state-of-the-art results. Our human evaluation shows that our method generates diverse, fluent, and informative summarized questions.

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A New Public Corpus for Clinical Section Identification: MedSecId
Paul Landes | Kunal Patel | Sean S. Huang | Adam Webb | Barbara Di Eugenio | Cornelia Caragea
Proceedings of the 29th International Conference on Computational Linguistics

The process by which sections in a document are demarcated and labeled is known as section identification. Such sections are helpful to the reader when searching for information and contextualizing specific topics. The goal of this work is to segment the sections of clinical medical domain documentation. The primary contribution of this work is MedSecId, a publicly available set of 2,002 fully annotated medical notes from the MIMIC-III. We include several baselines, source code, a pretrained model and analysis of the data showing a relationship between medical concepts across sections using principal component analysis.

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A Data Cartography based MixUp for Pre-trained Language Models
Seo Yeon Park | Cornelia Caragea
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

MixUp is a data augmentation strategy where additional samples are generated during training by combining random pairs of training samples and their labels. However, selecting random pairs is not potentially an optimal choice. In this work, we propose TDMixUp, a novel MixUp strategy that leverages Training Dynamics and allows more informative samples to be combined for generating new data samples. Our proposed TDMixUp first measures confidence, variability, (Swayamdipta et al., 2020), and Area Under the Margin (AUM) (Pleiss et al., 2020) to identify the characteristics of training samples (e.g., as easy-to-learn or ambiguous samples), and then interpolates these characterized samples. We empirically validate that our method not only achieves competitive performance using a smaller subset of the training data compared with strong baselines, but also yields lower expected calibration error on the pre-trained language model, BERT, on both in-domain and out-of-domain settings in a wide range of NLP tasks. We publicly release our code.

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Hierarchical Multi-Label Classification of Scientific Documents
Mobashir Sadat | Cornelia Caragea
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Automatic topic classification has been studied extensively to assist managing and indexing scientific documents in a digital collection. With the large number of topics being available in recent years, it has become necessary to arrange them in a hierarchy. Therefore, the automatic classification systems need to be able to classify the documents hierarchically. In addition, each paper is often assigned to more than one relevant topic. For example, a paper can be assigned to several topics in a hierarchy tree. In this paper, we introduce a new dataset for hierarchical multi-label text classification (HMLTC) of scientific papers called SciHTC, which contains 186,160 papers and 1,234 categories from the ACM CCS tree. We establish strong baselines for HMLTC and propose a multi-task learning approach for topic classification with keyword labeling as an auxiliary task. Our best model achieves a Macro-F1 score of 34.57% which shows that this dataset provides significant research opportunities on hierarchical scientific topic classification. We make our dataset and code for all experiments publicly available.

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Calibrating Student Models for Emotion-related Tasks
Mahshid Hosseini | Cornelia Caragea
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Knowledge Distillation (KD) is an effective method to transfer knowledge from one network (a.k.a. teacher) to another (a.k.a. student). In this paper, we study KD on the emotion-related tasks from a new perspective: calibration. We further explore the impact of the mixup data augmentation technique on the distillation objective and propose to use a simple yet effective mixup method informed by training dynamics for calibrating the student models. Underpinned by the regularization impact of the mixup process by providing better training signals to the student models using training dynamics, our proposed mixup strategy gradually enhances the student model’s calibration while effectively improving its performance. We evaluate the calibration of pre-trained language models through knowledge distillation over three tasks of emotion detection, sentiment analysis, and empathy detection. By conducting extensive experiments on different datasets, with both in-domain and out-of-domain test sets, we demonstrate that student models distilled from teacher models trained using our proposed mixup method obtained the lowest Expected Calibration Errors (ECEs) and best performance on both in-domain and out-of-domain test sets.

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Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts
Hongli Zhan | Tiberiu Sosea | Cornelia Caragea | Junyi Jessy Li
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Crises such as the COVID-19 pandemic continuously threaten our world and emotionally affect billions of people worldwide in distinct ways. Understanding the triggers leading to people’s emotions is of crucial importance. Social media posts can be a good source of such analysis, yet these texts tend to be charged with multiple emotions, with triggers scattering across multiple sentences. This paper takes a novel angle, namely, emotion detection and trigger summarization, aiming to both detect perceived emotions in text, and summarize events and their appraisals that trigger each emotion. To support this goal, we introduce CovidET (Emotions and their Triggers during Covid-19), a dataset of ~1,900 English Reddit posts related to COVID-19, which contains manual annotations of perceived emotions and abstractive summaries of their triggers described in the post. We develop strong baselines to jointly detect emotions and summarize emotion triggers. Our analyses show that CovidET presents new challenges in emotion-specific summarization, as well as multi-emotion detection in long social media posts.

2021

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Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers
Krutarth Patel | Cornelia Caragea
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Keyphrases associated with research papers provide an effective way to find useful information in the large and growing scholarly digital collections. In this paper, we present KPRank, an unsupervised graph-based algorithm for keyphrase extraction that exploits both positional information and contextual word embeddings into a biased PageRank. Our experimental results on five benchmark datasets show that KPRank that uses contextual word embeddings with additional position signal outperforms previous approaches and strong baselines for this task.

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Studying the Evolution of Scientific Topics and their Relationships
Ana Sabina Uban | Cornelia Caragea | Liviu P. Dinu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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A Multi-Task Learning Framework for Multi-Target Stance Detection
Yingjie Li | Cornelia Caragea
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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P-Stance: A Large Dataset for Stance Detection in Political Domain
Yingjie Li | Tiberiu Sosea | Aditya Sawant | Ajith Jayaraman Nair | Diana Inkpen | Cornelia Caragea
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Distilling Knowledge for Empathy Detection
Mahshid Hosseini | Cornelia Caragea
Findings of the Association for Computational Linguistics: EMNLP 2021

Empathy is the link between self and others. Detecting and understanding empathy is a key element for improving human-machine interaction. However, annotating data for detecting empathy at a large scale is a challenging task. This paper employs multi-task training with knowledge distillation to incorporate knowledge from available resources (emotion and sentiment) to detect empathy from the natural language in different domains. This approach yields better results on an existing news-related empathy dataset compared to strong baselines. In addition, we build a new dataset for empathy prediction with fine-grained empathy direction, seeking or providing empathy, from Twitter. We release our dataset for research purposes.

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Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation
Yingjie Li | Chenye Zhao | Cornelia Caragea
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Stance detection determines whether the author of a text is in favor of, against or neutral to a specific target and provides valuable insights into important events such as legalization of abortion. Despite significant progress on this task, one of the remaining challenges is the scarcity of annotations. Besides, most previous works focused on a hard-label training in which meaningful similarities among categories are discarded during training. To address these challenges, first, we evaluate a multi-target and a multi-dataset training settings by training one model on each dataset and datasets of different domains, respectively. We show that models can learn more universal representations with respect to targets in these settings. Second, we investigate the knowledge distillation in stance detection and observe that transferring knowledge from a teacher model to a student model can be beneficial in our proposed training settings. Moreover, we propose an Adaptive Knowledge Distillation (AKD) method that applies instance-specific temperature scaling to the teacher and student predictions. Results show that the multi-dataset model performs best on all datasets and it can be further improved by the proposed AKD, outperforming the state-of-the-art by a large margin. We publicly release our code.

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Target-Aware Data Augmentation for Stance Detection
Yingjie Li | Cornelia Caragea
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The goal of stance detection is to identify whether the author of a text is in favor of, neutral or against a specific target. Despite substantial progress on this task, one of the remaining challenges is the scarcity of annotations. Data augmentation is commonly used to address annotation scarcity by generating more training samples. However, the augmented sentences that are generated by existing methods are either less diversified or inconsistent with the given target and stance label. In this paper, we formulate the data augmentation of stance detection as a conditional masked language modeling task and augment the dataset by predicting the masked word conditioned on both its context and the auxiliary sentence that contains target and label information. Moreover, we propose another simple yet effective method that generates target-aware sentence by replacing a target mention with the other. Experimental results show that our proposed methods significantly outperforms previous augmentation methods on 11 targets.

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Identifying Medical Self-Disclosure in Online Communities
Mina Valizadeh | Pardis Ranjbar-Noiey | Cornelia Caragea | Natalie Parde
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Self-disclosure in online health conversations may offer a host of benefits, including earlier detection and treatment of medical issues that may have otherwise gone unaddressed. However, research analyzing medical self-disclosure in online communities is limited. We address this shortcoming by introducing a new dataset of health-related posts collected from online social platforms, categorized into three groups (No Self-Disclosure, Possible Self-Disclosure, and Clear Self-Disclosure) with high inter-annotator agreement (_k_=0.88). We make this data available to the research community. We also release a predictive model trained on this dataset that achieves an accuracy of 81.02%, establishing a strong performance benchmark for this task.

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Stance Detection in COVID-19 Tweets
Kyle Glandt | Sarthak Khanal | Yingjie Li | Doina Caragea | Cornelia Caragea
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

The prevalence of the COVID-19 pandemic in day-to-day life has yielded large amounts of stance detection data on social media sites, as users turn to social media to share their views regarding various issues related to the pandemic, e.g. stay at home mandates and wearing face masks when out in public. We set out to make use of this data by collecting the stance expressed by Twitter users, with respect to topics revolving around the pandemic. We annotate a new stance detection dataset, called COVID-19-Stance. Using this newly annotated dataset, we train several established stance detection models to ascertain a baseline performance for this specific task. To further improve the performance, we employ self-training and domain adaptation approaches to take advantage of large amounts of unlabeled data and existing stance detection datasets. The dataset, code, and other resources are available on GitHub.

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eMLM: A New Pre-training Objective for Emotion Related Tasks
Tiberiu Sosea | Cornelia Caragea
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

BERT has been shown to be extremely effective on a wide variety of natural language processing tasks, including sentiment analysis and emotion detection. However, the proposed pretraining objectives of BERT do not induce any sentiment or emotion-specific biases into the model. In this paper, we present Emotion Masked Language Modelling, a variation of Masked Language Modelling aimed at improving the BERT language representation model for emotion detection and sentiment analysis tasks. Using the same pre-training corpora as the original model, Wikipedia and BookCorpus, our BERT variation manages to improve the downstream performance on 4 tasks from emotion detection and sentiment analysis by an average of 1.2% F-1. Moreover, our approach shows an increased performance in our task-specific robustness tests.

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Knowledge Distillation with BERT for Image Tag-Based Privacy Prediction
Chenye Zhao | Cornelia Caragea
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Text in the form of tags associated with online images is often informative for predicting private or sensitive content from images. When using privacy prediction systems running on social networking sites that decide whether each uploaded image should get posted or be protected, users may be reluctant to share real images that may reveal their identity but may share image tags. In such cases, privacy-aware tags become good indicators of image privacy and can be utilized to generate privacy decisions. In this paper, our aim is to learn tag representations for images to improve tag-based image privacy prediction. To achieve this, we explore self-distillation with BERT, in which we utilize knowledge in the form of soft probability distributions (soft labels) from the teacher model to help with the training of the student model. Our approach effectively learns better tag representations with improved performance on private image identification and outperforms state-of-the-art models for this task. Moreover, we utilize the idea of knowledge distillation to improve tag representations in a semi-supervised learning task. Our semi-supervised approach with only 20% of annotated data achieves similar performance compared with its supervised learning counterpart. Last, we provide a comprehensive analysis to get a better understanding of our approach.

2020

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CancerEmo: A Dataset for Fine-Grained Emotion Detection
Tiberiu Sosea | Cornelia Caragea
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Emotions are an important element of human nature, often affecting the overall wellbeing of a person. Therefore, it is no surprise that the health domain is a valuable area of interest for emotion detection, as it can provide medical staff or caregivers with essential information about patients. However, progress on this task has been hampered by the absence of large labeled datasets. To this end, we introduce CancerEmo, an emotion dataset created from an online health community and annotated with eight fine-grained emotions. We perform a comprehensive analysis of these emotions and develop deep learning models on the newly created dataset. Our best BERT model achieves an average F1 of 71%, which we improve further using domain-specific pre-training.

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Dynamic Classification in Web Archiving Collections
Krutarth Patel | Cornelia Caragea | Mark Phillips
Proceedings of the Twelfth Language Resources and Evaluation Conference

The Web archived data usually contains high-quality documents that are very useful for creating specialized collections of documents. To create such collections, there is a substantial need for automatic approaches that can distinguish the documents of interest for a collection out of the large collections (of millions in size) from Web Archiving institutions. However, the patterns of the documents of interest can differ substantially from one document to another, which makes the automatic classification task very challenging. In this paper, we explore dynamic fusion models to find, on the fly, the model or combination of models that performs best on a variety of document types. Our experimental results show that the approach that fuses different models outperforms individual models and other ensemble methods on three datasets.

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Detecting Perceived Emotions in Hurricane Disasters
Shrey Desai | Cornelia Caragea | Junyi Jessy Li
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Natural disasters (e.g., hurricanes) affect millions of people each year, causing widespread destruction in their wake. People have recently taken to social media websites (e.g., Twitter) to share their sentiments and feelings with the larger community. Consequently, these platforms have become instrumental in understanding and perceiving emotions at scale. In this paper, we introduce HurricaneEmo, an emotion dataset of 15,000 English tweets spanning three hurricanes: Harvey, Irma, and Maria. We present a comprehensive study of fine-grained emotions and propose classification tasks to discriminate between coarse-grained emotion groups. Our best BERT model, even after task-guided pre-training which leverages unlabeled Twitter data, achieves only 68% accuracy (averaged across all groups). HurricaneEmo serves not only as a challenging benchmark for models but also as a valuable resource for analyzing emotions in disaster-centric domains.

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Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup
Jishnu Ray Chowdhury | Cornelia Caragea | Doina Caragea
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Distinguishing informative and actionable messages from a social media platform like Twitter is critical for facilitating disaster management. For this purpose, we compile a multilingual dataset of over 130K samples for multi-label classification of disaster-related tweets. We present a masking-based loss function for partially labelled samples and demonstrate the effectiveness of Manifold Mixup in the text domain. Our main model is based on Multilingual BERT, which we further improve with Manifold Mixup. We show that our model generalizes to unseen disasters in the test set. Furthermore, we analyze the capability of our model for zero-shot generalization to new languages. Our code, dataset, and other resources are available on Github.

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On the Use of Web Search to Improve Scientific Collections
Krutarth Patel | Cornelia Caragea | Sujatha Das Gollapalli
Proceedings of the First Workshop on Scholarly Document Processing

Despite the advancements in search engine features, ranking methods, technologies, and the availability of programmable APIs, current-day open-access digital libraries still rely on crawl-based approaches for acquiring their underlying document collections. In this paper, we propose a novel search-driven framework for acquiring documents for such scientific portals. Within our framework, publicly-available research paper titles and author names are used as queries to a Web search engine. We were able to obtain ~267,000 unique research papers through our fully-automated framework using ~76,000 queries, resulting in almost 200,000 more papers than the number of queries. Moreover, through a combination of title and author name search, we were able to recover 78% of the original searched titles.

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Scientific Keyphrase Identification and Classification by Pre-Trained Language Models Intermediate Task Transfer Learning
Seoyeon Park | Cornelia Caragea
Proceedings of the 28th International Conference on Computational Linguistics

Scientific keyphrase identification and classification is the task of detecting and classifying keyphrases from scholarly text with their types from a set of predefined classes. This task has a wide range of benefits, but it is still challenging in performance due to the lack of large amounts of labeled data required for training deep neural models. In order to overcome this challenge, we explore pre-trained language models BERT and SciBERT with intermediate task transfer learning, using 42 data-rich related intermediate-target task combinations. We reveal that intermediate task transfer learning on SciBERT induces a better starting point for target task fine-tuning compared with BERT and achieves competitive performance in scientific keyphrase identification and classification compared to both previous works and strong baselines. Interestingly, we observe that BERT with intermediate task transfer learning fails to improve the performance of scientific keyphrase identification and classification potentially due to significant catastrophic forgetting. This result highlights that scientific knowledge achieved during the pre-training of language models on large scientific collections plays an important role in the target tasks. We also observe that sequence tagging related intermediate tasks, especially syntactic structure learning tasks such as POS Tagging, tend to work best for scientific keyphrase identification and classification.

2019

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The Myth of Double-Blind Review Revisited: ACL vs. EMNLP
Cornelia Caragea | Ana Uban | Liviu P. Dinu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The review and selection process for scientific paper publication is essential for the quality of scholarly publications in a scientific field. The double-blind review system, which enforces author anonymity during the review period, is widely used by prestigious conferences and journals to ensure the integrity of this process. Although the notion of anonymity in the double-blind review has been questioned before, the availability of full text paper collections brings new opportunities for exploring the question: Is the double-blind review process really double-blind? We study this question on the ACL and EMNLP paper collections and present an analysis on how well deep learning techniques can infer the authors of a paper. Specifically, we explore Convolutional Neural Networks trained on various aspects of a paper, e.g., content, style features, and references, to understand the extent to which we can infer the authors of a paper and what aspects contribute the most. Our results show that the authors of a paper can be inferred with accuracy as high as 87% on ACL and 78% on EMNLP for the top 100 most prolific authors.

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Multi-Task Stance Detection with Sentiment and Stance Lexicons
Yingjie Li | Cornelia Caragea
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Stance detection aims to detect whether the opinion holder is in support of or against a given target. Recent works show improvements in stance detection by using either the attention mechanism or sentiment information. In this paper, we propose a multi-task framework that incorporates target-specific attention mechanism and at the same time takes sentiment classification as an auxiliary task. Moreover, we used a sentiment lexicon and constructed a stance lexicon to provide guidance for the attention layer. Experimental results show that the proposed model significantly outperforms state-of-the-art deep learning methods on the SemEval-2016 dataset.

2018

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Exploring Optimism and Pessimism in Twitter Using Deep Learning
Cornelia Caragea | Liviu P. Dinu | Bogdan Dumitru
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need such support, and for minimizing the negative influence among users and maximizing the spread of positive attitudes and ideas. In this paper, we explore a range of deep learning models to predict optimism and pessimism in Twitter at both tweet and user level and show that these models substantially outperform traditional machine learning classifiers used in prior work. In addition, we show evidence that a sentiment classifier would not be sufficient for accurately predicting optimism and pessimism in Twitter. Last, we study the verb tense usage as well as the presence of polarity words in optimistic and pessimistic tweets.

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Fine-Grained Emotion Detection in Health-Related Online Posts
Hamed Khanpour | Cornelia Caragea
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Detecting fine-grained emotions in online health communities provides insightful information about patients’ emotional states. However, current computational approaches to emotion detection from health-related posts focus only on identifying messages that contain emotions, with no emphasis on the emotion type, using a set of handcrafted features. In this paper, we take a step further and propose to detect fine-grained emotion types from health-related posts and show how high-level and abstract features derived from deep neural networks combined with lexicon-based features can be employed to detect emotions.

2017

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PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents
Corina Florescu | Cornelia Caragea
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The large and growing amounts of online scholarly data present both challenges and opportunities to enhance knowledge discovery. One such challenge is to automatically extract a small set of keyphrases from a document that can accurately describe the document’s content and can facilitate fast information processing. In this paper, we propose PositionRank, an unsupervised model for keyphrase extraction from scholarly documents that incorporates information from all positions of a word’s occurrences into a biased PageRank. Our model obtains remarkable improvements in performance over PageRank models that do not take into account word positions as well as over strong baselines for this task. Specifically, on several datasets of research papers, PositionRank achieves improvements as high as 29.09%.

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Identifying Empathetic Messages in Online Health Communities
Hamed Khanpour | Cornelia Caragea | Prakhar Biyani
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Empathy captures one’s ability to correlate with and understand others’ emotional states and experiences. Messages with empathetic content are considered as one of the main advantages for joining online health communities due to their potential to improve people’s moods. Unfortunately, to this date, no computational studies exist that automatically identify empathetic messages in online health communities. We propose a combination of Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks, and show that the proposed model outperforms each individual model (CNN and LSTM) as well as several baselines.

2016

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Supervised Keyphrase Extraction as Positive Unlabeled Learning
Lucas Sterckx | Cornelia Caragea | Thomas Demeester | Chris Develder
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Co-Training for Topic Classification of Scholarly Data
Cornelia Caragea | Florin Bulgarov | Rada Mihalcea
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the ACL 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction
Sujatha Das Gollapalli | Cornelia Caragea | Xiaoli Li | C. Lee Giles
Proceedings of the ACL 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction

2014

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Identifying Emotional and Informational Support in Online Health Communities
Prakhar Biyani | Cornelia Caragea | Prasenjit Mitra | John Yen
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach
Cornelia Caragea | Florin Adrian Bulgarov | Andreea Godea | Sujatha Das Gollapalli
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2012

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Thread Specific Features are Helpful for Identifying Subjectivity Orientation of Online Forum Threads
Prakhar Biyani | Sumit Bhatia | Cornelia Caragea | Prasenjit Mitra
Proceedings of COLING 2012