Omri Abend


2020

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Classifying Syntactic Errors in Learner Language
Leshem Choshen | Dmitry Nikolaev | Yevgeni Berzak | Omri Abend
Proceedings of the 24th Conference on Computational Natural Language Learning

We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence. The methodology builds on the established Universal Dependencies syntactic representation scheme, and provides complementary information to other error-classification systems. Unlike existing error classification methods, our method is applicable across languages, which we showcase by producing a detailed picture of syntactic errors in learner English and learner Russian. We further demonstrate the utility of the methodology for analyzing the outputs of leading Grammatical Error Correction (GEC) systems.

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Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing
Stephan Oepen | Omri Abend | Lasha Abzianidze | Johan Bos | Jan Hajič | Daniel Hershcovich | Bin Li | Tim O'Gorman | Nianwen Xue | Daniel Zeman
Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing

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MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing
Stephan Oepen | Omri Abend | Lasha Abzianidze | Johan Bos | Jan Hajic | Daniel Hershcovich | Bin Li | Tim O’Gorman | Nianwen Xue | Daniel Zeman
Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing

The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages. Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework. The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://mrp.nlpl.eu

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Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences
Dmitry Nikolaev | Ofir Arviv | Taelin Karidi | Neta Kenneth | Veronika Mitnik | Lilja Maria Saeboe | Omri Abend
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The patterns in which the syntax of different languages converges and diverges are often used to inform work on cross-lingual transfer. Nevertheless, little empirical work has been done on quantifying the prevalence of different syntactic divergences across language pairs. We propose a framework for extracting divergence patterns for any language pair from a parallel corpus, building on Universal Dependencies. We show that our framework provides a detailed picture of cross-language divergences, generalizes previous approaches, and lends itself to full automation. We further present a novel dataset, a manually word-aligned subset of the Parallel UD corpus in five languages, and use it to perform a detailed corpus study. We demonstrate the usefulness of the resulting analysis by showing that it can help account for performance patterns of a cross-lingual parser.

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Language (Re)modelling: Towards Embodied Language Understanding
Ronen Tamari | Chen Shani | Tom Hope | Miriam R L Petruck | Omri Abend | Dafna Shahaf
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

While natural language understanding (NLU) is advancing rapidly, today’s technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization. This work proposes an approach to representation and learning based on the tenets of embodied cognitive linguistics (ECL). According to ECL, natural language is inherently executable (like programming languages), driven by mental simulation and metaphoric mappings over hierarchical compositions of structures and schemata learned through embodied interaction. This position paper argues that the use of grounding by metaphoric reasoning and simulation will greatly benefit NLU systems, and proposes a system architecture along with a roadmap towards realizing this vision.

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Machine Reading of Historical Events
Or Honovich | Lucas Torroba Hennigen | Omri Abend | Shay B. Cohen
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Machine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities. Within this broad framework, we address the task of machine reading the time of historical events, compile datasets for the task, and develop a model for tackling it. Given a brief textual description of an event, we show that good performance can be achieved by extracting relevant sentences from Wikipedia, and applying a combination of task-specific and general-purpose feature embeddings for the classification. Furthermore, we establish a link between the historical event ordering task and the event focus time task from the information retrieval literature, showing they also provide a challenging test case for machine reading algorithms.

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Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics
Daniel Hershcovich | Nathan Schneider | Dotan Dvir | Jakob Prange | Miryam de Lhoneux | Omri Abend
Proceedings of the 28th International Conference on Computational Linguistics

Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other. To perform a systematic comparative analysis, we evaluate the mapping between meaning representations from different frameworks using two complementary methods: (i) a rule-based converter, and (ii) a supervised delexicalized parser that parses to one framework using only information from the other as features. We apply these methods to convert the STREUSLE corpus (with syntactic and lexical semantic annotations) to UCCA (a graph-structured full-sentence meaning representation). Both methods yield surprisingly accurate target representations, close to fully supervised UCCA parser quality—indicating that UCCA annotations are partially redundant with STREUSLE annotations. Despite this substantial convergence between frameworks, we find several important areas of divergence.

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Cross-lingual Semantic Representation for NLP with UCCA
Omri Abend | Dotan Dvir | Daniel Hershcovich | Jakob Prange | Nathan Schneider
Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts

This is an introductory tutorial to UCCA (Universal Conceptual Cognitive Annotation), a cross-linguistically applicable framework for semantic representation, with corpora annotated in English, German and French, and ongoing annotation in Russian and Hebrew. UCCA builds on extensive typological work and supports rapid annotation. The tutorial will provide a detailed introduction to the UCCA annotation guidelines, design philosophy and the available resources; and a comparison to other meaning representations. It will also survey the existing parsing work, including the findings of three recent shared tasks, in SemEval and CoNLL, that addressed UCCA parsing. Finally, the tutorial will present recent applications and extensions to the scheme, demonstrating its value for natural language processing in a range of languages and domains.

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Semantic Structural Decomposition for Neural Machine Translation
Elior Sulem | Omri Abend | Ari Rappoport
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

Building on recent advances in semantic parsing and text simplification, we investigate the use of semantic splitting of the source sentence as preprocessing for machine translation. We experiment with a Transformer model and evaluate using large-scale crowd-sourcing experiments. Results show a significant increase in fluency on long sentences on an English-to- French setting with a training corpus of 5M sentence pairs, while retaining comparable adequacy. We also perform a manual analysis which explores the tradeoff between adequacy and fluency in the case where all sentence lengths are considered.

2019

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Made for Each Other: Broad-Coverage Semantic Structures Meet Preposition Supersenses
Jakob Prange | Nathan Schneider | Omri Abend
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013) is a typologically-informed, broad-coverage semantic annotation scheme that describes coarse-grained predicate-argument structure but currently lacks semantic roles. We argue that lexicon-free annotation of the semantic roles marked by prepositions, as formulated by Schneider et al. (2018), is complementary and suitable for integration within UCCA. We show empirically for English that the schemes, though annotated independently, are compatible and can be combined in a single semantic graph. A comparison of several approaches to parsing the integrated representation lays the groundwork for future research on this task.

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Automatically Extracting Challenge Sets for Non-Local Phenomena in Neural Machine Translation
Leshem Choshen | Omri Abend
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

We show that the state-of-the-art Transformer MT model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model. Since most dependencies are short-distance, common evaluation metrics will be little influenced by how well systems perform on them. We therefore propose an automatic approach for extracting challenge sets rich with long-distance dependencies, and argue that evaluation using this methodology provides a complementary perspective on system performance. To support our claim, we compile challenge sets for English-German and German-English, which are much larger than any previously released challenge set for MT. The extracted sets are large enough to allow reliable automatic evaluation, which makes the proposed approach a scalable and practical solution for evaluating MT performance on the long-tail of syntactic phenomena.

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Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning
Stephan Oepen | Omri Abend | Jan Hajic | Daniel Hershcovich | Marco Kuhlmann | Tim O’Gorman | Nianwen Xue
Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

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MRP 2019: Cross-Framework Meaning Representation Parsing
Stephan Oepen | Omri Abend | Jan Hajic | Daniel Hershcovich | Marco Kuhlmann | Tim O’Gorman | Nianwen Xue | Jayeol Chun | Milan Straka | Zdenka Uresova
Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

The 2019 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks. Five distinct approaches to the representation of sentence meaning in the form of directed graph were represented in the training and evaluation data for the task, packaged in a uniform abstract graph representation and serialization. The task received submissions from eighteen teams, of which five do not participate in the official ranking because they arrived after the closing deadline, made use of additional training data, or involved one of the task co-organizers. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://mrp.nlpl.eu

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The Language of Legal and Illegal Activity on the Darknet
Leshem Choshen | Dan Eldad | Daniel Hershcovich | Elior Sulem | Omri Abend
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity. Given the magnitude of these networks, scalably monitoring their activity necessarily relies on automated tools, and notably on NLP tools. However, little is known about what characteristics texts communicated through the Darknet have, and how well do off-the-shelf NLP tools do on this domain. This paper tackles this gap and performs an in-depth investigation of the characteristics of legal and illegal text in the Darknet, comparing it to a clear net website with similar content as a control condition. Taking drugs-related websites as a test case, we find that texts for selling legal and illegal drugs have several linguistic characteristics that distinguish them from one another, as well as from the control condition, among them the distribution of POS tags, and the coverage of their named entities in Wikipedia.

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Preparing SNACS for Subjects and Objects
Adi Shalev | Jena D. Hwang | Nathan Schneider | Vivek Srikumar | Omri Abend | Ari Rappoport
Proceedings of the First International Workshop on Designing Meaning Representations

Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens. Importantly, that work has argued for a principled separation of the semantic role in a scene from the function coded by morphosyntax. Here, we ask whether this approach can be generalized beyond adpositions and possessives to cover all scene participants—including subjects and objects—directly, without reference to a frame lexicon. We present new guidelines for English and the results of an interannotator agreement study.

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Semantically Constrained Multilayer Annotation: The Case of Coreference
Jakob Prange | Nathan Schneider | Omri Abend
Proceedings of the First International Workshop on Designing Meaning Representations

We propose a coreference annotation scheme as a layer on top of the Universal Conceptual Cognitive Annotation foundational layer, treating units in predicate-argument structure as a basis for entity and event mentions. We argue that this allows coreference annotators to sidestep some of the challenges faced in other schemes, which do not enforce consistency with predicate-argument structure and vary widely in what kinds of mentions they annotate and how. The proposed approach is examined with a pilot annotation study and compared with annotations from other schemes.

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Content Differences in Syntactic and Semantic Representation
Daniel Hershcovich | Omri Abend | Ari Rappoport
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)

Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of empirical comparative studies between syntactic and semantic schemes, which hinders the development of parsing methods informed by the details of target schemes and constructions. We target this gap, and take Universal Dependencies (UD) and UCCA as a test case. After abstracting away from differences of convention or formalism, we find that most content divergences can be ascribed to: (1) UCCA’s distinction between a Scene and a non-Scene; (2) UCCA’s distinction between primary relations, secondary ones and participants; (3) different treatment of multi-word expressions, and (4) different treatment of inter-clause linkage. We further discuss the long tail of cases where the two schemes take markedly different approaches. Finally, we show that the proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement. The substantial differences between the schemes suggest that semantic parsers are likely to benefit downstream text understanding applications beyond their syntactic counterparts.

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SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA
Daniel Hershcovich | Zohar Aizenbud | Leshem Choshen | Elior Sulem | Ari Rappoport | Omri Abend
Proceedings of the 13th International Workshop on Semantic Evaluation

We present the SemEval 2019 shared task on Universal Conceptual Cognitive Annotation (UCCA) parsing in English, German and French, and discuss the participating systems and results. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. The shared task has yielded improvements over the state-of-the-art baseline in all languages and settings. Full results can be found in the task’s website https://competitions.codalab.org/competitions/19160.

2018

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Universal Dependency Parsing with a General Transition-Based DAG Parser
Daniel Hershcovich | Omri Abend | Ari Rappoport
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task. TUPA is a general neural transition-based DAG parser, which we use to present the first experiments on recovering enhanced dependencies as part of the general parsing task. TUPA was designed for parsing UCCA, a cross-linguistic semantic annotation scheme, exhibiting reentrancy, discontinuity and non-terminal nodes. By converting UD trees and graphs to a UCCA-like DAG format, we train TUPA almost without modification on the UD parsing task. The generic nature of our approach lends itself naturally to multitask learning.

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Semantic Structural Evaluation for Text Simplification
Elior Sulem | Omri Abend | Ari Rappoport
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Current measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects. In this paper we propose the first measure to address structural aspects of text simplification, called SAMSA. It leverages recent advances in semantic parsing to assess simplification quality by decomposing the input based on its semantic structure and comparing it to the output. SAMSA provides a reference-less automatic evaluation procedure, avoiding the problems that reference-based methods face due to the vast space of valid simplifications for a given sentence. Our human evaluation experiments show both SAMSA’s substantial correlation with human judgments, as well as the deficiency of existing reference-based measures in evaluating structural simplification.

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Reference-less Measure of Faithfulness for Grammatical Error Correction
Leshem Choshen | Omri Abend
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We propose USim, a semantic measure for Grammatical Error Correction (that measures the semantic faithfulness of the output to the source, thereby complementing existing reference-less measures (RLMs) for measuring the output’s grammaticality. USim operates by comparing the semantic symbolic structure of the source and the correction, without relying on manually-curated references. Our experiments establish the validity of USim, by showing that the semantic structures can be consistently applied to ungrammatical text, that valid corrections obtain a high USim similarity score to the source, and that invalid corrections obtain a lower score.

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BLEU is Not Suitable for the Evaluation of Text Simplification
Elior Sulem | Omri Abend | Ari Rappoport
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS). TS includes both lexical and structural aspects. In this paper we show that BLEU is not suitable for the evaluation of sentence splitting, the major structural simplification operation. We manually compiled a sentence splitting gold standard corpus containing multiple structural paraphrases, and performed a correlation analysis with human judgments. We find low or no correlation between BLEU and the grammaticality and meaning preservation parameters where sentence splitting is involved. Moreover, BLEU often negatively correlates with simplicity, essentially penalizing simpler sentences.

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Simple and Effective Text Simplification Using Semantic and Neural Methods
Elior Sulem | Omri Abend | Ari Rappoport
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used in this situation. Previous application of Machine Translation for simplification suffers from a considerable disadvantage in that they are over-conservative, often failing to modify the source in any way. Splitting based on semantic parsing, as proposed here, alleviates this issue. Extensive automatic and human evaluation shows that the proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification.

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Comprehensive Supersense Disambiguation of English Prepositions and Possessives
Nathan Schneider | Jena D. Hwang | Vivek Srikumar | Jakob Prange | Austin Blodgett | Sarah R. Moeller | Aviram Stern | Adi Bitan | Omri Abend
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Semantic relations are often signaled with prepositional or possessive marking—but extreme polysemy bedevils their analysis and automatic interpretation. We introduce a new annotation scheme, corpus, and task for the disambiguation of prepositions and possessives in English. Unlike previous approaches, our annotations are comprehensive with respect to types and tokens of these markers; use broadly applicable supersense classes rather than fine-grained dictionary definitions; unite prepositions and possessives under the same class inventory; and distinguish between a marker’s lexical contribution and the role it marks in the context of a predicate or scene. Strong interannotator agreement rates, as well as encouraging disambiguation results with established supervised methods, speak to the viability of the scheme and task.

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Multitask Parsing Across Semantic Representations
Daniel Hershcovich | Omri Abend | Ari Rappoport
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others. In this paper we tackle the challenging task of improving semantic parsing performance, taking UCCA parsing as a test case, and AMR, SDP and Universal Dependencies (UD) parsing as auxiliary tasks. We experiment on three languages, using a uniform transition-based system and learning architecture for all parsing tasks. Despite notable conceptual, formal and domain differences, we show that multitask learning significantly improves UCCA parsing in both in-domain and out-of-domain settings.

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Inherent Biases in Reference-based Evaluation for Grammatical Error Correction
Leshem Choshen | Omri Abend
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality (henceforth, low coverage bias or LCB). This paper shows that overcoming LCB in Grammatical Error Correction (GEC) evaluation cannot be attained by re-scaling or by increasing the number of references in any feasible range, contrary to previous suggestions. This is due to the long-tailed distribution of valid corrections for a sentence. Concretely, we show that LCB incentivizes GEC systems to avoid correcting even when they can generate a valid correction. Consequently, existing systems obtain comparable or superior performance compared to humans, by making few but targeted changes to the input. Similar effects on Text Simplification further support our claims.

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Automatic Metric Validation for Grammatical Error Correction
Leshem Choshen | Omri Abend
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Metric validation in Grammatical Error Correction (GEC) is currently done by observing the correlation between human and metric-induced rankings. However, such correlation studies are costly, methodologically troublesome, and suffer from low inter-rater agreement. We propose MAEGE, an automatic methodology for GEC metric validation, that overcomes many of the difficulties in the existing methodology. Experiments with MAEGE shed a new light on metric quality, showing for example that the standard M2 metric fares poorly on corpus-level ranking. Moreover, we use MAEGE to perform a detailed analysis of metric behavior, showing that some types of valid edits are consistently penalized by existing metrics.

2017

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The State of the Art in Semantic Representation
Omri Abend | Ari Rappoport
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Semantic representation is receiving growing attention in NLP in the past few years, and many proposals for semantic schemes (e.g., AMR, UCCA, GMB, UDS) have been put forth. Yet, little has been done to assess the achievements and the shortcomings of these new contenders, compare them with syntactic schemes, and clarify the general goals of research on semantic representation. We address these gaps by critically surveying the state of the art in the field.

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A Transition-Based Directed Acyclic Graph Parser for UCCA
Daniel Hershcovich | Omri Abend | Ari Rappoport
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our knowledge, the conjunction of these formal properties is not supported by any existing parser. Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.

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UCCAApp: Web-application for Syntactic and Semantic Phrase-based Annotation
Omri Abend | Shai Yerushalmi | Ari Rappoport
Proceedings of ACL 2017, System Demonstrations

2016

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HUME: Human UCCA-Based Evaluation of Machine Translation
Alexandra Birch | Omri Abend | Ondřej Bojar | Barry Haddow
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Conceptual Annotations Preserve Structure Across Translations: A French-English Case Study
Elior Sulem | Omri Abend | Ari Rappoport
Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation (S2MT 2015)

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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

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)

2013

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UCCA: A Semantics-based Grammatical Annotation Scheme
Omri Abend | Ari Rappoport
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers

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Universal Conceptual Cognitive Annotation (UCCA)
Omri Abend | Ari Rappoport
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP
Omri Abend | Chris Biemann | Anna Korhonen | Ari Rappoport | Roi Reichart | Anders Søgaard
Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP

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Learnability-Based Syntactic Annotation Design
Roy Schwartz | Omri Abend | Ari Rappoport
Proceedings of COLING 2012

2011

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Proceedings of the First workshop on Unsupervised Learning in NLP
Omri Abend | Anna Korhonen | Ari Rappoport | Roi Reichart
Proceedings of the First workshop on Unsupervised Learning in NLP

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Neutralizing Linguistically Problematic Annotations in Unsupervised Dependency Parsing Evaluation
Roy Schwartz | Omri Abend | Roi Reichart | Ari Rappoport
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Type Level Clustering Evaluation: New Measures and a POS Induction Case Study
Roi Reichart | Omri Abend | Ari Rappoport
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

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Fully Unsupervised Core-Adjunct Argument Classification
Omri Abend | Ari Rappoport
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Improved Unsupervised POS Induction through Prototype Discovery
Omri Abend | Roi Reichart | Ari Rappoport
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

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Unsupervised Argument Identification for Semantic Role Labeling
Omri Abend | Roi Reichart | Ari Rappoport
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

2008

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A Supervised Algorithm for Verb Disambiguation into VerbNet Classes
Omri Abend | Roi Reichart | Ari Rappoport
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)