Shay B. Cohen

Also published as: Shay Cohen


2019

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Wide-Coverage Neural A* Parsing for Minimalist Grammars
John Torr | Milos Stanojevic | Mark Steedman | Shay B. Cohen
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Minimalist Grammars (Stabler, 1997) are a computationally oriented, and rigorous formalisation of many aspects of Chomsky’s (1995) Minimalist Program. This paper presents the first ever application of this formalism to the task of realistic wide-coverage parsing. The parser uses a linguistically expressive yet highly constrained grammar, together with an adaptation of the A* search algorithm currently used in CCG parsing (Lewis and Steedman, 2014; Lewis et al., 2016), with supertag probabilities provided by a bi-LSTM neural network supertagger trained on MGbank, a corpus of MG derivation trees. We report on some promising initial experimental results for overall dependency recovery as well as on the recovery of certain unbounded long distance dependencies. Finally, although like other MG parsers, ours has a high order polynomial worst case time complexity, we show that in practice its expected time complexity is cubic in the length of the sentence. The parser is publicly available.

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Duality of Link Prediction and Entailment Graph Induction
Mohammad Javad Hosseini | Shay B. Cohen | Mark Johnson | Mark Steedman
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Link prediction and entailment graph induction are often treated as different problems. In this paper, we show that these two problems are actually complementary. We train a link prediction model on a knowledge graph of assertions extracted from raw text. We propose an entailment score that exploits the new facts discovered by the link prediction model, and then form entailment graphs between relations. We further use the learned entailments to predict improved link prediction scores. Our results show that the two tasks can benefit from each other. The new entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction scores.

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Discourse Representation Parsing for Sentences and Documents
Jiangming Liu | Shay B. Cohen | Mirella Lapata
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We introduce a novel semantic parsing task based on Discourse Representation Theory (DRT; Kamp and Reyle 1993). Our model operates over Discourse Representation Tree Structures which we formally define for sentences and documents. We present a general framework for parsing discourse structures of arbitrary length and granularity. We achieve this with a neural model equipped with a supervised hierarchical attention mechanism and a linguistically-motivated copy strategy. Experimental results on sentence- and document-level benchmarks show that our model outperforms competitive baselines by a wide margin.

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Semantic Role Labeling with Iterative Structure Refinement
Chunchuan Lyu | Shay B. Cohen | Ivan Titov
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Modern state-of-the-art Semantic Role Labeling (SRL) methods rely on expressive sentence encoders (e.g., multi-layer LSTMs) but tend to model only local (if any) interactions between individual argument labeling decisions. This contrasts with earlier work and also with the intuition that the labels of individual arguments are strongly interdependent. We model interactions between argument labeling decisions through iterative refinement. Starting with an output produced by a factorized model, we iteratively refine it using a refinement network. Instead of modeling arbitrary interactions among roles and words, we encode prior knowledge about the SRL problem by designing a restricted network architecture capturing non-local interactions. This modeling choice prevents overfitting and results in an effective model, outperforming strong factorized baseline models on all 7 CoNLL-2009 languages, and achieving state-of-the-art results on 5 of them, including English.

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Partners in Crime: Multi-view Sequential Inference for Movie Understanding
Nikos Papasarantopoulos | Lea Frermann | Mirella Lapata | Shay B. Cohen
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Multi-view learning algorithms are powerful representation learning tools, often exploited in the context of multimodal problems. However, for problems requiring inference at the token-level of a sequence (that is, a separate prediction must be made for every time step), it is often the case that single-view systems are used, or that more than one views are fused in a simple manner. We describe an incremental neural architecture paired with a novel training objective for incremental inference. The network operates on multi-view data. We demonstrate the effectiveness of our approach on the problem of predicting perpetrators in crime drama series, for which our model significantly outperforms previous work and strong baselines. Moreover, we introduce two tasks, crime case and speaker type tagging, that contribute to movie understanding and demonstrate the effectiveness of our model on them.

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Experimenting with Power Divergences for Language Modeling
Matthieu Labeau | Shay B. Cohen
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Neural language models are usually trained using Maximum-Likelihood Estimation (MLE). The corresponding objective function for MLE is derived from the Kullback-Leibler (KL) divergence between the empirical probability distribution representing the data and the parametric probability distribution output by the model. However, the word frequency discrepancies in natural language make performance extremely uneven: while the perplexity is usually very low for frequent words, it is especially difficult to predict rare words. In this paper, we experiment with several families (alpha, beta and gamma) of power divergences, generalized from the KL divergence, for learning language models with an objective different than standard MLE. Intuitively, these divergences should affect the way the probability mass is spread during learning, notably by prioritizing performances on high or low-frequency words. In addition, we implement and experiment with various sampling-based objectives, where the computation of the output layer is only done on a small subset of the vocabulary. They are derived as power generalizations of a softmax approximated via Importance Sampling, and Noise Contrastive Estimation, for accelerated learning. Our experiments on the Penn Treebank and Wikitext-2 show that these power divergences can indeed be used to prioritize learning on the frequent or rare words, and lead to general performance improvements in the case of sampling-based learning.

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Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model
Jiangming Liu | Shay B. Cohen | Mirella Lapata
Proceedings of the IWCS Shared Task on Semantic Parsing

We describe the systems we developed for Discourse Representation Structure (DRS) parsing as part of the IWCS-2019 Shared Task of DRS Parsing.1 Our systems are based on sequence-to-sequence modeling. To implement our model, we use the open-source neural machine translation system implemented in PyTorch, OpenNMT-py. We experimented with a variety of encoder-decoder models based on recurrent neural networks and the Transformer model. We conduct experiments on the standard benchmark of the Parallel Meaning Bank (PMB 2.2). Our best system achieves a score of 84.8% F1 in the DRS parsing shared task.

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Bottom-Up Unranked Tree-to-Graph Transducers for Translation into Semantic Graphs
Johanna Björklund | Shay B. Cohen | Frank Drewes | Giorgio Satta
Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing

We propose a formal model for translating unranked syntactic trees, such as dependency trees, into semantic graphs. These tree-to-graph transducers can serve as a formal basis of transition systems for semantic parsing which recently have been shown to perform very well, yet hitherto lack formalization. Our model features “extended” rules and an arc-factored normal form, comes with an efficient translation algorithm, and can be equipped with weights in a straightforward manner.

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Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle
Maximin Coavoux | Shay B. Cohen
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We introduce a novel transition system for discontinuous constituency parsing. Instead of storing subtrees in a stack –i.e. a data structure with linear-time sequential access– the proposed system uses a set of parsing items, with constant-time random access. This change makes it possible to construct any discontinuous constituency tree in exactly 4n–2 transitions for a sentence of length n. At each parsing step, the parser considers every item in the set to be combined with a focus item and to construct a new constituent in a bottom-up fashion. The parsing strategy is based on the assumption that most syntactic structures can be parsed incrementally and that the set –the memory of the parser– remains reasonably small on average. Moreover, we introduce a provably correct dynamic oracle for the new transition system, and present the first experiments in discontinuous constituency parsing using a dynamic oracle. Our parser obtains state-of-the-art results on three English and German discontinuous treebanks.

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Structural Neural Encoders for AMR-to-text Generation
Marco Damonte | Shay B. Cohen
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

AMR-to-text generation is a problem recently introduced to the NLP community, in which the goal is to generate sentences from Abstract Meaning Representation (AMR) graphs. Sequence-to-sequence models can be used to this end by converting the AMR graphs to strings. Approaching the problem while working directly with graphs requires the use of graph-to-sequence models that encode the AMR graph into a vector representation. Such encoding has been shown to be beneficial in the past, and unlike sequential encoding, it allows us to explicitly capture reentrant structures in the AMR graphs. We investigate the extent to which reentrancies (nodes with multiple parents) have an impact on AMR-to-text generation by comparing graph encoders to tree encoders, where reentrancies are not preserved. We show that improvements in the treatment of reentrancies and long-range dependencies contribute to higher overall scores for graph encoders. Our best model achieves 24.40 BLEU on LDC2015E86, outperforming the state of the art by 1.1 points and 24.54 BLEU on LDC2017T10, outperforming the state of the art by 1.24 points.

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Jointly Extracting and Compressing Documents with Summary State Representations
Afonso Mendes | Shashi Narayan | Sebastião Miranda | Zita Marinho | André F. T. Martins | Shay B. Cohen
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We present a new neural model for text summarization that first extracts sentences from a document and then compresses them. The pro-posed model offers a balance that sidesteps thedifficulties in abstractive methods while gener-ating more concise summaries than extractivemethods. In addition, our model dynamically determines the length of the output summary based on the gold summaries it observes during training and does not require length constraints typical to extractive summarization. The model achieves state-of-the-art results on the CNN/DailyMail and Newsroom datasets, improving over current extractive and abstractive methods. Human evaluations demonstratethat our model generates concise and informa-tive summaries. We also make available a new dataset of oracle compressive summaries derived automatically from the CNN/DailyMailreference summaries.

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Unlexicalized Transition-based Discontinuous Constituency Parsing
Maximin Coavoux | Benoît Crabbé | Shay B. Cohen
Transactions of the Association for Computational Linguistics, Volume 7

Lexicalized parsing models are based on the assumptions that (i) constituents are organized around a lexical head and (ii) bilexical statistics are crucial to solve ambiguities. In this paper, we introduce an unlexicalized transition-based parser for discontinuous constituency structures, based on a structure-label transition system and a bi-LSTM scoring system. We compare it with lexicalized parsing models in order to address the question of lexicalization in the context of discontinuous constituency parsing. Our experiments show that unlexicalized models systematically achieve higher results than lexicalized models, and provide additional empirical evidence that lexicalization is not necessary to achieve strong parsing results. Our best unlexicalized model sets a new state of the art on English and German discontinuous constituency treebanks. We further provide a per-phenomenon analysis of its errors on discontinuous constituents.

2018

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Local String Transduction as Sequence Labeling
Joana Ribeiro | Shashi Narayan | Shay B. Cohen | Xavier Carreras
Proceedings of the 27th International Conference on Computational Linguistics

We show that the general problem of string transduction can be reduced to the problem of sequence labeling. While character deletion and insertions are allowed in string transduction, they do not exist in sequence labeling. We show how to overcome this difference. Our approach can be used with any sequence labeling algorithm and it works best for problems in which string transduction imposes a strong notion of locality (no long range dependencies). We experiment with spelling correction for social media, OCR correction, and morphological inflection, and we see that it behaves better than seq2seq models and yields state-of-the-art results in several cases.

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Privacy-preserving Neural Representations of Text
Maximin Coavoux | Shashi Narayan | Shay B. Cohen
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This article deals with adversarial attacks towards deep learning systems for Natural Language Processing (NLP), in the context of privacy protection. We study a specific type of attack: an attacker eavesdrops on the hidden representations of a neural text classifier and tries to recover information about the input text. Such scenario may arise in situations when the computation of a neural network is shared across multiple devices, e.g. some hidden representation is computed by a user’s device and sent to a cloud-based model. We measure the privacy of a hidden representation by the ability of an attacker to predict accurately specific private information from it and characterize the tradeoff between the privacy and the utility of neural representations. Finally, we propose several defense methods based on modified training objectives and show that they improve the privacy of neural representations.

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Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
Shashi Narayan | Shay B. Cohen | Mirella Lapata
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We introduce “extreme summarization”, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question “What is the article about?”. We collect a real-world, large-scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans.

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Multilingual Clustering of Streaming News
Sebastião Miranda | Artūrs Znotiņš | Shay B. Cohen | Guntis Barzdins
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Clustering news across languages enables efficient media monitoring by aggregating articles from multilingual sources into coherent stories. Doing so in an online setting allows scalable processing of massive news streams. To this end, we describe a novel method for clustering an incoming stream of multilingual documents into monolingual and crosslingual clusters. Unlike typical clustering approaches that report results on datasets with a small and known number of labels, we tackle the problem of discovering an ever growing number of cluster labels in an online fashion, using real news datasets in multiple languages. In our formulation, the monolingual clusters group together documents while the crosslingual clusters group together monolingual clusters, one per language that appears in the stream. Our method is simple to implement, computationally efficient and produces state-of-the-art results on datasets in German, English and Spanish.

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Whodunnit? Crime Drama as a Case for Natural Language Understanding
Lea Frermann | Shay B. Cohen | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 6

In this paper we argue that crime drama exemplified in television programs such as CSI: Crime Scene Investigation is an ideal testbed for approximating real-world natural language understanding and the complex inferences associated with it. We propose to treat crime drama as a new inference task, capitalizing on the fact that each episode poses the same basic question (i.e., who committed the crime) and naturally provides the answer when the perpetrator is revealed. We develop a new dataset based on CSI episodes, formalize perpetrator identification as a sequence labeling problem, and develop an LSTM-based model which learns from multi-modal data. Experimental results show that an incremental inference strategy is key to making accurate guesses as well as learning from representations fusing textual, visual, and acoustic input.

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Learning Typed Entailment Graphs with Global Soft Constraints
Mohammad Javad Hosseini | Nathanael Chambers | Siva Reddy | Xavier R. Holt | Shay B. Cohen | Mark Johnson | Mark Steedman
Transactions of the Association for Computational Linguistics, Volume 6

This paper presents a new method for learning typed entailment graphs from text. We extract predicate-argument structures from multiple-source news corpora, and compute local distributional similarity scores to learn entailments between predicates with typed arguments (e.g., person contracted disease). Previous work has used transitivity constraints to improve local decisions, but these constraints are intractable on large graphs. We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph. Learning takes only a few hours to run over 100K predicates and our results show large improvements over local similarity scores on two entailment data sets. We further show improvements over paraphrases and entailments from the Paraphrase Database, and prior state-of-the-art entailment graphs. We show that the entailment graphs improve performance in a downstream task.

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Discourse Representation Structure Parsing
Jiangming Liu | Shay B. Cohen | Mirella Lapata
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce an open-domain neural semantic parser which generates formal meaning representations in the style of Discourse Representation Theory (DRT; Kamp and Reyle 1993). We propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages: basic DRS structure prediction, condition prediction (i.e., predicates and relations), and referent prediction (i.e., variables). Experimental results on the Groningen Meaning Bank (GMB) show that our model outperforms competitive baselines by a wide margin.

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Stock Movement Prediction from Tweets and Historical Prices
Yumo Xu | Shay B. Cohen
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Stock movement prediction is a challenging problem: the market is highly stochastic, and we make temporally-dependent predictions from chaotic data. We treat these three complexities and present a novel deep generative model jointly exploiting text and price signals for this task. Unlike the case with discriminative or topic modeling, our model introduces recurrent, continuous latent variables for a better treatment of stochasticity, and uses neural variational inference to address the intractable posterior inference. We also provide a hybrid objective with temporal auxiliary to flexibly capture predictive dependencies. We demonstrate the state-of-the-art performance of our proposed model on a new stock movement prediction dataset which we collected.

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Document Modeling with External Attention for Sentence Extraction
Shashi Narayan | Ronald Cardenas | Nikos Papasarantopoulos | Shay B. Cohen | Mirella Lapata | Jiangsheng Yu | Yi Chang
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Document modeling is essential to a variety of natural language understanding tasks. We propose to use external information to improve document modeling for problems that can be framed as sentence extraction. We develop a framework composed of a hierarchical document encoder and an attention-based extractor with attention over external information. We evaluate our model on extractive document summarization (where the external information is image captions and the title of the document) and answer selection (where the external information is a question). We show that our model consistently outperforms strong baselines, in terms of both informativeness and fluency (for CNN document summarization) and achieves state-of-the-art results for answer selection on WikiQA and NewsQA.

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Abstract Meaning Representation for Paraphrase Detection
Fuad Issa | Marco Damonte | Shay B. Cohen | Xiaohui Yan | Yi Chang
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form. As such, it is ideal for paraphrase detection, a problem in which one is required to specify whether two sentences have the same meaning. We show that naïve use of AMR in paraphrase detection is not necessarily useful, and turn to describe a technique based on latent semantic analysis in combination with AMR parsing that significantly advances state-of-the-art results in paraphrase detection for the Microsoft Research Paraphrase Corpus. Our best results in the transductive setting are 86.6% for accuracy and 90.0% for F1 measure.

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Cross-Lingual Abstract Meaning Representation Parsing
Marco Damonte | Shay B. Cohen
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Abstract Meaning Representation (AMR) research has mostly focused on English. We show that it is possible to use AMR annotations for English as a semantic representation for sentences written in other languages. We exploit an AMR parser for English and parallel corpora to learn AMR parsers for Italian, Spanish, German and Chinese. Qualitative analysis show that the new parsers overcome structural differences between the languages. We further propose a method to evaluate the parsers that does not require gold standard data in the target languages. This method highly correlates with the gold standard evaluation, obtaining a Pearson correlation coefficient of 0.95.

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Ranking Sentences for Extractive Summarization with Reinforcement Learning
Shashi Narayan | Shay B. Cohen | Mirella Lapata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Single document summarization is the task of producing a shorter version of a document while preserving its principal information content. In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. We use our algorithm to train a neural summarization model on the CNN and DailyMail datasets and demonstrate experimentally that it outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans.

2017

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Proceedings of the 2nd Workshop on Representation Learning for NLP
Phil Blunsom | Antoine Bordes | Kyunghyun Cho | Shay Cohen | Chris Dyer | Edward Grefenstette | Karl Moritz Hermann | Laura Rimell | Jason Weston | Scott Yih
Proceedings of the 2nd Workshop on Representation Learning for NLP

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Latent-Variable PCFGs: Background and Applications
Shay Cohen
Proceedings of the 15th Meeting on the Mathematics of Language

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Split and Rephrase
Shashi Narayan | Claire Gardent | Shay B. Cohen | Anastasia Shimorina
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We propose a new sentence simplification task (Split-and-Rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences. Like sentence simplification, splitting-and-rephrasing has the potential of benefiting both natural language processing and societal applications. Because shorter sentences are generally better processed by NLP systems, it could be used as a preprocessing step which facilitates and improves the performance of parsers, semantic role labellers and machine translation systems. It should also be of use for people with reading disabilities because it allows the conversion of longer sentences into shorter ones. This paper makes two contributions towards this new task. First, we create and make available a benchmark consisting of 1,066,115 tuples mapping a single complex sentence to a sequence of sentences expressing the same meaning. Second, we propose five models (vanilla sequence-to-sequence to semantically-motivated models) to understand the difficulty of the proposed task.

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An Incremental Parser for Abstract Meaning Representation
Marco Damonte | Shay B. Cohen | Giorgio Satta
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Abstract Meaning Representation (AMR) is a semantic representation for natural language that embeds annotations related to traditional tasks such as named entity recognition, semantic role labeling, word sense disambiguation and co-reference resolution. We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time. We further propose a test-suite that assesses specific subtasks that are helpful in comparing AMR parsers, and show that our parser is competitive with the state of the art on the LDC2015E86 dataset and that it outperforms state-of-the-art parsers for recovering named entities and handling polarity.

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The SUMMA Platform Prototype
Renars Liepins | Ulrich Germann | Guntis Barzdins | Alexandra Birch | Steve Renals | Susanne Weber | Peggy van der Kreeft | Hervé Bourlard | João Prieto | Ondřej Klejch | Peter Bell | Alexandros Lazaridis | Alfonso Mendes | Sebastian Riedel | Mariana S. C. Almeida | Pedro Balage | Shay B. Cohen | Tomasz Dwojak | Philip N. Garner | Andreas Giefer | Marcin Junczys-Dowmunt | Hina Imran | David Nogueira | Ahmed Ali | Sebastião Miranda | Andrei Popescu-Belis | Lesly Miculicich Werlen | Nikos Papasarantopoulos | Abiola Obamuyide | Clive Jones | Fahim Dalvi | Andreas Vlachos | Yang Wang | Sibo Tong | Rico Sennrich | Nikolaos Pappas | Shashi Narayan | Marco Damonte | Nadir Durrani | Sameer Khurana | Ahmed Abdelali | Hassan Sajjad | Stephan Vogel | David Sheppey | Chris Hernon | Jeff Mitchell
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.

2016

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Parsing Linear Context-Free Rewriting Systems with Fast Matrix Multiplication
Shay B. Cohen | Daniel Gildea
Computational Linguistics, Volume 42, Issue 3 - September 2016

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Optimizing Spectral Learning for Parsing
Shashi Narayan | Shay B. Cohen
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the 1st Workshop on Representation Learning for NLP
Phil Blunsom | Kyunghyun Cho | Shay Cohen | Edward Grefenstette | Karl Moritz Hermann | Laura Rimell | Jason Weston | Scott Wen-tau Yih
Proceedings of the 1st Workshop on Representation Learning for NLP

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Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing
Shashi Narayan | Siva Reddy | Shay B. Cohen
Proceedings of the 9th International Natural Language Generation conference

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Semi-Supervised Learning of Sequence Models with Method of Moments
Zita Marinho | André F. T. Martins | Shay B. Cohen | Noah A. Smith
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Encoding Prior Knowledge with Eigenword Embeddings
Dominique Osborne | Shashi Narayan | Shay B. Cohen
Transactions of the Association for Computational Linguistics, Volume 4

Canonical correlation analysis (CCA) is a method for reducing the dimension of data represented using two views. It has been previously used to derive word embeddings, where one view indicates a word, and the other view indicates its context. We describe a way to incorporate prior knowledge into CCA, give a theoretical justification for it, and test it by deriving word embeddings and evaluating them on a myriad of datasets.

2015

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Conversation Trees: A Grammar Model for Topic Structure in Forums
Annie Louis | Shay B. Cohen
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Diversity in Spectral Learning for Natural Language Parsing
Shashi Narayan | Shay B. Cohen
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Lexical Event Ordering with an Edge-Factored Model
Omri Abend | Shay B. Cohen | Mark Steedman
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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A Coactive Learning View of Online Structured Prediction in Statistical Machine Translation
Artem Sokolov | Stefan Riezler | Shay B. Cohen
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

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Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing
Phil Blunsom | Shay Cohen | Paramveer Dhillon | Percy Liang
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

2014

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Lexical Inference over Multi-Word Predicates: A Distributional Approach
Omri Abend | Shay B. Cohen | Mark Steedman
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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A Provably Correct Learning Algorithm for Latent-Variable PCFGs
Shay B. Cohen | Michael Collins
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Spectral Unsupervised Parsing with Additive Tree Metrics
Ankur P. Parikh | Shay B. Cohen | Eric P. Xing
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Online Adaptor Grammars with Hybrid Inference
Ke Zhai | Jordan Boyd-Graber | Shay B. Cohen
Transactions of the Association for Computational Linguistics, Volume 2

Adaptor grammars are a flexible, powerful formalism for defining nonparametric, unsupervised models of grammar productions. This flexibility comes at the cost of expensive inference. We address the difficulty of inference through an online algorithm which uses a hybrid of Markov chain Monte Carlo and variational inference. We show that this inference strategy improves scalability without sacrificing performance on unsupervised word segmentation and topic modeling tasks.

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Latent-Variable Synchronous CFGs for Hierarchical Translation
Avneesh Saluja | Chris Dyer | Shay B. Cohen
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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The effect of non-tightness on Bayesian estimation of PCFGs
Shay B. Cohen | Mark Johnson
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Experiments with Spectral Learning of Latent-Variable PCFGs
Shay B. Cohen | Karl Stratos | Michael Collins | Dean P. Foster | Lyle Ungar
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Approximate PCFG Parsing Using Tensor Decomposition
Shay B. Cohen | Giorgio Satta | Michael Collins
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Spectral Learning Algorithms for Natural Language Processing
Shay Cohen | Michael Collins | Dean Foster | Karl Stratos | Lyle Ungar
NAACL HLT 2013 Tutorial Abstracts

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Spectral Learning of Refinement HMMs
Karl Stratos | Alexander Rush | Shay B. Cohen | Michael Collins
Proceedings of the Seventeenth Conference on Computational Natural Language Learning

2012

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Spectral Learning of Latent-Variable PCFGs
Shay B. Cohen | Karl Stratos | Michael Collins | Dean P. Foster | Lyle Ungar
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Empirical Risk Minimization for Probabilistic Grammars: Sample Complexity and Hardness of Learning
Shay B. Cohen | Noah A. Smith
Computational Linguistics, Volume 38, Issue 3 - September 2012

2011

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Unsupervised Bilingual POS Tagging with Markov Random Fields
Desai Chen | Chris Dyer | Shay Cohen | Noah Smith
Proceedings of the First workshop on Unsupervised Learning in NLP

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Unsupervised Structure Prediction with Non-Parallel Multilingual Guidance
Shay B. Cohen | Dipanjan Das | Noah A. Smith
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Exact Inference for Generative Probabilistic Non-Projective Dependency Parsing
Shay B. Cohen | Carlos Gómez-Rodríguez | Giorgio Satta
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Social Links from Latent Topics in Microblogs
Kriti Puniyani | Jacob Eisenstein | Shay B. Cohen | Eric Xing
Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media

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Viterbi Training for PCFGs: Hardness Results and Competitiveness of Uniform Initialization
Shay Cohen | Noah A. Smith
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Variational Inference for Adaptor Grammars
Shay B. Cohen | David M. Blei | Noah A. Smith
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2009

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Shared Logistic Normal Distributions for Soft Parameter Tying in Unsupervised Grammar Induction
Shay Cohen | Noah A. Smith
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Variational Inference for Grammar Induction with Prior Knowledge
Shay Cohen | Noah A. Smith
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

2007

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Joint Morphological and Syntactic Disambiguation
Shay B. Cohen | Noah A. Smith
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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