Shift-Reduce CCG Parsing using Neural Network Models

Ambati, Bharat Ram; Deoskar, Tejaswini; Steedman, Mark

doi:10.18653/v1/n16-1052

Cited by 11 publications

(11 citation statements)

References 19 publications

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“…Transition-based techniques are a natural starting point for UCCA parsing, given the conceptual similarity of UCCA's distinctions, centered around predicate-argument structures, to distinctions expressed by dependency schemes, and the achievements of transition-based methods in dependency parsing (Dyer et al, 2015;Andor et al, 2016;Kiperwasser and Goldberg, 2016). We are further motivated by the strength of transition-based methods in related tasks, including dependency graph parsing (Sagae and Tsujii, 2008;Ribeyre et al, 2014;Tokgöz and Eryigit, 2015), constituency parsing (Sagae and Lavie, 2005;Zhang and Clark, 2009;Zhu et al, 2013;Maier, 2015;Maier and Lichte, 2016), AMR parsing (Wang et al, 2015a(Wang et al, ,b, 2016Misra and Artzi, 2016;Goodman et al, 2016;Zhou et al, 2016;Damonte et al, 2017) and CCG parsing (Zhang and Clark, 2011;Ambati et al, 2015Ambati et al, , 2016.…”

Section: Introductionmentioning

confidence: 99%

A Transition-Based Directed Acyclic Graph Parser for UCCA

Hershcovich¹,

Abend²,

Rappoport³

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

109

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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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Section: Introductionmentioning

confidence: 99%

A Transition-Based Directed Acyclic Graph Parser for UCCA

Hershcovich¹,

Abend²,

Rappoport³

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

109

View full text Add to dashboard Cite

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“…Most of the work over the last years on deep parsing using neural network architectures has been done for the CCG formalism and for English language. For example (Xu et al, 2015) uses a recurrent neural network for improving the supertagging and parsing accuracy for CCG, while (Ambati et al, 2016) describes a neural networks architecture that performs CCG parsing. For HPSG, the work by (Zhou and Zhao, 2019) is very relevant as they try to derive a HPSG grammar from the Penn Treebanks in English and Chinese and use an self-attention based mechanism followed by a CKY decoder to parse them, obtaining very good results.…”

Section: Related Workmentioning

confidence: 99%

Statistical Deep Parsing for Spanish Using Neural Networks

Chiruzzo

Wonsever

2020

Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing Into Enhanced

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This paper presents the development of a deep parser for Spanish that uses a HPSG grammar and returns trees that contain both syntactic and semantic information. The parsing process uses a top-down approach implemented using LSTM neural networks, and achieves good performance results in terms of syntactic constituency and dependency metrics, and also SRL. We describe the grammar, corpus and implementation of the parser. Our process outperforms a CKY baseline and other Spanish parsers in terms of global metrics and also for some specific Spanish phenomena, such as clitics reduplication and relative referents.

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“…Most of the work over the last years on deep parsing using neural network architectures has been done for the CCG formalism and for English language. For example (Xu et al, 2015) uses a recurrent neural network for improving the supertagging and parsing accuracy for CCG, while (Ambati et al, 2016) describes a neural networks architecture that performs CCG parsing. For HPSG, the work by ) is very relevant as they try to derive a HPSG grammar from the Penn Treebanks in English and Chinese and use an self-attention based mechanism followed by a CKY decoder to parse them, obtaining very good results.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

2020

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Welcome to the 16th International Conference on Parsing Technologies (IWPT 2020), which this year (for the first time since 2007) is co-located with the Annual Meeting of the Association for Computational Linguistics (ACL). The IWPT meeting series, hosted by the ACL Special Interest Group in Natural Language Parsing (SIGPARSE), has been held biennualy since its inaugual meeting in 1989 in Pittsburgh, PA (USA).For 2020, the SIGPARSE steering group decided to try out something new, co-location with the main ACL meeting in the form of a reduced one-day IWPT programme. The main motivation for this move was to reduce fragmentation (and travel) and to increase IWPT visibility in the 'mainstream' ACL community (we already know that at least one of these goals was attained). At the same time, IWPT launches its own series of parsing shared tasks this year, which strengthens the experimental and applied perspective on parsing technologies in the conference programme.The IWPT 2020 shared task focuses on the parsing of Enhanced Universal Dependencies (EUD) over 17 languages. This is the first time that graph-based representations of syntactic structures are evaluated on such a large scale, and we believe it will pave way for research on richer models and representations. The task attracted system submissions from ten teams from around the world and, thus, establishes a highly relevant point of comparison for this line of syntactic analysis. We are very grateful to everyone who contributed to this shared task, starting with the data providers who worked hard to meet our deadline. Thanks to the participant teams who worked tirelessly in a short time period to provide such a set of great and interesting systems! Owing to the COVID-19 pandemic this year, the meeting will regrettably be held entirely virtual, where for IWPT we have adopted a mostly-asynchronous format: Accepted papers (of three different types: long, short, and shared task) will be presented through pre-recorded talks, which become available online for individual viewing before the actual conference day. On the original date of the conference, July 9, there will be a three-hour live session, scheduled so that the timing should be convenient (all things considered) for participants around the world: 14:00-17:00 UTC, which translates, for example, into a starting time at 7:00 in the morning at the US West Coast and wrapping up at 1:00 in the morning in Melbourne, Australia. The live sessions will be devoted exclusively to questions and answers, organized into five thematic sessions. Authors of papers associated with each session will be available to answer questions and disucss their work (possibly also among themselves).There has been (and to some degree still is) much uncertainty about the format of ACL and IWPT this year, and in a sense we were positively surprised to receive a number of submissions comparable to recent IWPT instances. Out of 24 regular paper submissions, the programme committee accepted 14 for presentation at the conference. The IWPT 2020 progr...

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Shift-Reduce CCG Parsing using Neural Network Models

Cited by 11 publications

References 19 publications

A Transition-Based Directed Acyclic Graph Parser for UCCA

A Transition-Based Directed Acyclic Graph Parser for UCCA

Statistical Deep Parsing for Spanish Using Neural Networks

Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

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