Semantic graph parsing with recurrent neural network DAG grammars

Fancellu, Federico; Gilroy, Sorcha; Lopez, Adam; Lapata, Mirella

doi:10.18653/v1/d19-1278

Cited by 19 publications

(28 citation statements)

References 26 publications

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“…Interestingly, the two-encoder setup seems to be preferable for these smaller, non-English data sets. For 2.2.0, we outperform the system of Fancellu et al (2019) for German and Italian and obtain competitive scores for Dutch.…”

Section: Robustness Across Languagesmentioning

confidence: 90%

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: Robustness Across Languagesmentioning

confidence: 90%

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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“…Most approaches that produce semantic graphs (see for an overview) model distributions over graphs directly (Dozat and Manning, 2018;Zhang et al, 2019;He and Choi, 2020;Cai and Lam, 2020), while others make use of derivation trees that compositionally evaluate to graphs (Groschwitz et al, 2018;Chen et al, 2018;Fancellu et al, 2019;Lindemann et al, 2019). AM dependency parsing belongs to the latter category.…”

Section: Related Workmentioning

confidence: 99%

Fast semantic parsing with well-typedness guarantees

Lindemann¹,

Groschwitz²,

Koller³

2020

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

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AM dependency parsing is a linguistically principled method for neural semantic parsing with high accuracy across multiple graphbanks. It relies on a type system that models semantic valency but makes existing parsers slow. We describe an A* parser and a transition-based parser for AM dependency parsing which guarantee well-typedness and improve parsing speed by up to 3 orders of magnitude, while maintaining or improving accuracy.

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“…Because AMR are naturally structured objects (e.g. tree structures), semantic AMR parsing methods based on deep graph generative models are deemed as promising [19,35,88,130,145]. These methods represent the semantics of a sentence as a semantic graph (i.e., a sub-graph of a knowledge base) and treat semantic parsing as a semantic graph matching/generation process.…”

Section: Semantic Parsingmentioning

confidence: 99%

A Systematic Survey on Deep Generative Models for Graph Generation

Guo¹,

Zhao²

2020

Preprint

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Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.

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Semantic graph parsing with recurrent neural network DAG grammars

Cited by 19 publications

References 26 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

Fast semantic parsing with well-typedness guarantees

A Systematic Survey on Deep Generative Models for Graph Generation

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