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    <abstract>This paper proposes a data representation framework for semantic parsing and task-oriented dialogue systems, aiming to achieve a uniform representation for syntactically and semantically diverse machine-readable formats.Current NLP systems heavily rely on adapting pre-trained language models to specific tasks, and this approach has been proven effective for modeling natural language texts.However, little attention has been paid to the representation of machine-readable formats, such as database queries and dialogue states.We present a method for converting original machine-readable formats of semantic parsing and task-oriented dialogue datasets into a syntactically and semantically uniform representation.We define a meta grammar for syntactically uniform representations and translate semantically equivalent functions into a uniform vocabulary.Empirical experiments on 13 datasets show that accuracy consistently improves over original formats, revealing the advantage of the proposed representation.Additionally, we show that the proposed representation allows for transfer learning across datasets.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.60</identifier>
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%0 Conference Proceedings
%T Syntactic and Semantic Uniformity for Semantic Parsing and Task-Oriented Dialogue Systems
%A Chen, Bowen
%A Miyao, Yusuke
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F chen-miyao-2022-syntactic
%X This paper proposes a data representation framework for semantic parsing and task-oriented dialogue systems, aiming to achieve a uniform representation for syntactically and semantically diverse machine-readable formats.Current NLP systems heavily rely on adapting pre-trained language models to specific tasks, and this approach has been proven effective for modeling natural language texts.However, little attention has been paid to the representation of machine-readable formats, such as database queries and dialogue states.We present a method for converting original machine-readable formats of semantic parsing and task-oriented dialogue datasets into a syntactically and semantically uniform representation.We define a meta grammar for syntactically uniform representations and translate semantically equivalent functions into a uniform vocabulary.Empirical experiments on 13 datasets show that accuracy consistently improves over original formats, revealing the advantage of the proposed representation.Additionally, we show that the proposed representation allows for transfer learning across datasets.
%R 10.18653/v1/2022.findings-emnlp.60
%U https://aclanthology.org/2022.findings-emnlp.60/
%U https://doi.org/10.18653/v1/2022.findings-emnlp.60
%P 855-867

Markdown (Informal)

[Syntactic and Semantic Uniformity for Semantic Parsing and Task-Oriented Dialogue Systems](https://aclanthology.org/2022.findings-emnlp.60/) (Chen & Miyao, Findings 2022)

• Syntactic and Semantic Uniformity for Semantic Parsing and Task-Oriented Dialogue Systems (Chen & Miyao, Findings 2022)

ACL

• Bowen Chen and Yusuke Miyao. 2022. Syntactic and Semantic Uniformity for Semantic Parsing and Task-Oriented Dialogue Systems. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 855–867, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.