SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation

May, Jonathan; Priyadarshi, Jay

doi:10.18653/v1/s17-2090

Cited by 58 publications

(67 citation statements)

References 22 publications

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“…Tuning is performed on the respective development sets. For AMR, we use LDC2017T10, identical to the dataset targeted in SemEval 2017 (May and Priyadarshi, 2017). 9 For SDP, we use the DM representation from the SDP 2016 dataset (Oepen et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

Multitask Parsing Across Semantic Representations

Hershcovich¹,

Abend²,

Rappoport³

2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

105

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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. Our code is publicly available. 1

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Section: Methodsmentioning

confidence: 99%

Multitask Parsing Across Semantic Representations

Hershcovich¹,

Abend²,

Rappoport³

2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

105

View full text Add to dashboard Cite

show abstract

“…The surface realization task bears the closest resemblance to the SemEval 2017 shared task AMR-to-text (May and Priyadarshi, 2017). Our approach to data augmentation and preprocessing uses many insights from Neural AMR (Konstas et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models

Elder¹,

Hokamp²

2018

Proceedings of the First Workshop on Multilingual Surface Realisation

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This work presents a new state of the art in reconstruction of surface realizations from obfuscated text. We identify the lack of sufficient training data as the major obstacle to training high-performing models, and solve this issue by generating large amounts of synthetic training data. We also propose preprocessing techniques which make the structure contained in the input features more accessible to sequence models. Our models were ranked first on all evaluation metrics in the English portion of the 2018 Surface Realization shared task.

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“…At the same time, AMR frequently invokes lexical decomposition and represents some implicitly expressed elements of meaning, such that AMR graphs quite generally appear to 'abstract' furthest from the surface signal. Since the first general release of an AMR graph bank in 2014, the framework has provided a popular target for semantic parsing and has been the subject of two consecutive tasks at SemEval 2016 and 2017 (May, 2016;May and Priyadarshi, 2017).…”

Section: Tutorial Content and Relevancementioning

confidence: 99%

Graph-Based Meaning Representations: Design and Processing

Koller¹,

Oepen²,

Sun³

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

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This tutorial is on representing and processing sentence meaning in the form of labeled directed graphs. The tutorial will (a) briefly review relevant background in formal and linguistic semantics; (b) semi-formally define a unified abstract view on different flavors of semantic graphs and associated terminology; (c) survey common frameworks for graph-based meaning representation and available graph banks; and (d) offer a technical overview of a representative selection of different parsing approaches.

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SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation

Cited by 58 publications

References 22 publications

Multitask Parsing Across Semantic Representations

Multitask Parsing Across Semantic Representations

Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models

Graph-Based Meaning Representations: Design and Processing

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