SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA

Hershcovich, Daniel; Aizenbud, Zohar; Choshen, Leshem; Sulem, Elior; Rappoport, Ari; Abend, Omri

doi:10.18653/v1/s19-2001

Cited by 33 publications

(31 citation statements)

References 29 publications

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“…We also convert the original format to the unified format in preprocessing, and recover the original DAG format in post-processing. We evaluate our approach on three separate broadcoverage semantic parsing tasks: (1) AMR 2.0 (LDC2017T10) and 1.0 (LDC2014T12); (2) the English DM dataset from SemEval 2015 Task 18 (LDC2016T10); (3) the UCCA English Wikipedia Corpus v1.2 (Abend and Rappoport, 2013;Hershcovich et al, 2019). The train/dev/test split follows the official setup.…”

Section: Predictionmentioning

confidence: 99%

Broad-Coverage Semantic Parsing as Transduction

Zhang¹,

Ma²,

Duh³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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We unify different broad-coverage semantic parsing tasks under a transduction paradigm, and propose an attention-based neural framework that incrementally builds a meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the transducer can be effectively trained without relying on a pre-trained aligner. Experiments conducted on three separate broadcoverage semantic parsing tasks -AMR, SDP and UCCA -demonstrate that our attentionbased neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP. arXiv:1909.02607v2 [cs.CL] 4 Nov 2019 F1 on LDC2014T12) and UCCA (76.6% F1 on the English-Wiki dataset v1.2), and is competitive with the state of the art on SDP (92.2% F1 on the English DELPH-IN MRS dataset).

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Section: Predictionmentioning

confidence: 99%

Broad-Coverage Semantic Parsing as Transduction

Zhang¹,

Ma²,

Duh³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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“…Previous published results of applying TUPA to UCCA parsing (Hershcovich et al, 2017(Hershcovich et al, , 2018a(Hershcovich et al, , 2019b used a different version of the parser, without contextualized word representations from BERT. For comparability with previous results, we train and test an identical model to the one presented in this paper, on the SemEval 2019 Task 1 data (Hershcovich et al, 2019b), which is UCCA-only, but contains tracks in English, German and French. For this experiment, we use bert-multilingual instead of bertlarge-cased, and train a shared model over all three languages.…”

Section: Comparability With Previous Resultsmentioning

confidence: 99%

TUPA at MRP 2019: A Multi-Task Baseline System

Hershcovich¹,

Arviv²

2019

Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Lea

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This paper describes the TUPA system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). TUPA provides a baseline point of comparison and is not considered in the official ranking of participating systems. While originally developed for UCCA only, TUPA has been generalized to support all MRP frameworks included in the task, and trained using multi-task learning to parse them all with a shared model. It is a transition-based parser with a BiLSTM encoder, augmented with BERT contextualized embeddings.

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“…Semantic parsing has been gaining in popularity in the last few years. There have been a series of shared tasks in semantic parsing organized, where each task requires to generate meaning representations of specific types: Broad-Coverage Broad-coverage Semantic Dependencies (Oepen et al, 2014(Oepen et al, , 2015, Abstract Meaning Representation (May, 2016;May and Priyadarshi, 2017), or Universal Conceptual Cognitive Annotation (Hershcovich et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The First Shared Task on Discourse Representation Structure Parsing

Abzianidze¹,

Noord²,

Haagsma³

et al. 2019

Proceedings of the IWCS Shared Task on Semantic Parsing

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The paper presents the IWCS 2019 shared task on semantic parsing where the goal is to produce Discourse Representation Structures (DRSs) for English sentences. DRSs originate from Discourse Representation Theory and represent scoped meaning representations that capture the semantics of negation, modals, quantification, and presupposition triggers. Additionally, concepts and eventparticipants in DRSs are described with WordNet synsets and the thematic roles from VerbNet. To measure similarity between two DRSs, they are represented in a clausal form, i.e. as a set of tuples. Participant systems were expected to produce DRSs in this clausal form. Taking into account the rich lexical information, explicit scope marking, a high number of shared variables among clauses, and highly-constrained format of valid DRSs, all these makes the DRS parsing a challenging NLP task. The results of the shared task displayed improvements over the existing state-of-the-art parser. 6 http://github.com/RikVN/DRS_parsing 7 In Figure 5 redundant REF-clauses are stricken through.

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SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA

Cited by 33 publications

References 29 publications

Broad-Coverage Semantic Parsing as Transduction

Broad-Coverage Semantic Parsing as Transduction

TUPA at MRP 2019: A Multi-Task Baseline System

The First Shared Task on Discourse Representation Structure Parsing

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