DRTS Parsing with Structure-Aware Encoding and Decoding

Fu, Qiankun; Zhang, Yue; Liu, Jiangming; Zhang, Meishan

doi:10.18653/v1/2020.acl-main.609

Cited by 7 publications

(9 citation statements)

References 28 publications

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“…This model achieved impressive performance on both sentence-level and document-level DRS parsing on GMB data. Fu et al (2020) in turn improve on this work by employing a Graph Attention Network during both encoding and decoding. The introduction of gold standard DRSs in the PMB enabled a principled comparison of approaches.…”

Section: Discourse Representation Structuresmentioning

confidence: 91%

Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT

Noord¹,

Toral²,

Bos³

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing. Character representations can easily be added in a sequence-to-sequence model in either one encoder or as a fully separate encoder, with improvements that are robust to different language models, languages and data sets. For English, these improvements are larger than adding individual sources of linguistic information or adding non-contextual embeddings. A new method of analysis based on semantic tags demonstrates that the character-level representations improve performance across a subset of selected semantic phenomena.

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Section: Discourse Representation Structuresmentioning

confidence: 91%

Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT

Noord¹,

Toral²,

Bos³

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…Several data-driven methods based on neural networks have been proposed for DRS parsing (van Noord et al, 2018bLiu et al, 2019a;Evang, 2019;Fancellu et al, 2019;Fu et al, 2020;van Noord et al, 2020). These approaches frame semantic parsing as a sequence transformation problem and map the target meaning representation to string format.…”

Section: Introductionmentioning

confidence: 99%

Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese

Wang¹,

Noord²,

Bisazza³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Neural semantic parsers have obtained acceptable results in the context of parsing DRSs (Discourse Representation Structures). In particular models with character sequences as input showed remarkable performance for English. But how does this approach perform on languages with a different writing system, like Chinese, a language with a large vocabulary of characters? Does rule-based tokenisation of the input help, and which granularity is preferred: characters, or words? The results are promising. Even with DRSs based on English, good results for Chinese are obtained. Tokenisation offers a small advantage for English, but not for Chinese. Overall, characters are preferred as input, both for English and Chinese.

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“…This model achieved impressive performance on both sentence-level and document-level DRS parsing based on GMB data. Fu et al (2020) in turn improve on this work by employing a Graph Attention Network during both encoding and decoding.…”

Section: Drs Parsingmentioning

confidence: 91%

“…An initial release of this data was used by the cross-lingual CCG-based parser of Evang and Bos (2016). The PMB is described in more detail in Chapter 6, while we describe our own DRS parsers trained on PMB data in Chapters 7, 8 and 9, as well as other (contemporary) developed DRS parsers (Liu et al, 2018a(Liu et al, , 2019aFancellu et al, 2019;Fu et al, 2020).…”

Section: Drs Parsingmentioning

confidence: 99%

“…The most recently developed neural DRS parsers (Liu et al, 2018a(Liu et al, , 2019aFu et al, 2020) only evaluate on the GMB, making a comparison difficult. However, we consider it highly likely that their more structured models (based on the coarse-to-fine approach of Dong and Lapata, 2018) would outperform our more vanilla sequence-tosequence models.…”

Section: Drs Parsing On Gmb Datamentioning

confidence: 99%

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Character-based Neural Semantic Parsing

Noord¹

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I quite enjoyed my time as a PhD-student. This is largely due to all the people I spent time with over the past 4 years. I feel like I owe all of you a sincere thank you, so here goes.First, and most importantly, I'd like to thank Johan for hiring me and guiding me through the project. You gave me a lot of freedom, which I appreciated, but also gently pushed me back on track if needed. Let the record show that some of the more creative ideas in this thesis were yours. I really enjoyed working together, but also have great memories of the soccer matches and parties you hosted (despite your kids always beating me at chess).Antonio, asking you to be my co-supervisor was probably one of the best decisions I made during the PhD. Your knowledge about neural networks, eye for detail and pressing for experimental rigour made this a much better thesis. Above all, you were simply very pleasant to work with. Thanks a lot! Lasha, though not as an official supervisor, you helped me a lot during these 4 years (even co-authoring two of the papers) and I appreciate that.There is no one else out there who can be so clever, funny and confusing at the same time. I will surely always remember the "compensation cake".Of course, thanks to the reading committee, Michael Biehl, Anette Frank and Mirella Lapata, for reading and accepting my thesis. You provided me with a lot of useful feedback, which definitely improved the thesis.

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DRTS Parsing with Structure-Aware Encoding and Decoding

Cited by 7 publications

References 28 publications

Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT

Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT

Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese

Character-based Neural Semantic Parsing

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