@inproceedings{guo-etal-2020-benchmarking-meaning,
title = "Benchmarking Meaning Representations in Neural Semantic Parsing",
author = "Guo, Jiaqi and
Liu, Qian and
Lou, Jian-Guang and
Li, Zhenwen and
Liu, Xueqing and
Xie, Tao and
Liu, Ting",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.118",
doi = "10.18653/v1/2020.emnlp-main.118",
pages = "1520--1540",
abstract = "Meaning representation is an important component of semantic parsing. Although researchers have designed a lot of meaning representations, recent work focuses on only a few of them. Thus, the impact of meaning representation on semantic parsing is less understood. Furthermore, existing work{'}s performance is often not comprehensively evaluated due to the lack of readily-available execution engines. Upon identifying these gaps, we propose , a new unified benchmark on meaning representations, by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines. The resulting unified benchmark contains the complete enumeration of logical forms and execution engines over three datasets $\times$ four meaning representations. A thorough experimental study on Unimer reveals that neural semantic parsing approaches exhibit notably different performance when they are trained to generate different meaning representations. Also, program alias and grammar rules heavily impact the performance of different meaning representations. Our benchmark, execution engines and implementation can be found on: \url{https://github.com/JasperGuo/Unimer}.",
}
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<abstract>Meaning representation is an important component of semantic parsing. Although researchers have designed a lot of meaning representations, recent work focuses on only a few of them. Thus, the impact of meaning representation on semantic parsing is less understood. Furthermore, existing work’s performance is often not comprehensively evaluated due to the lack of readily-available execution engines. Upon identifying these gaps, we propose , a new unified benchmark on meaning representations, by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines. The resulting unified benchmark contains the complete enumeration of logical forms and execution engines over three datasets \times four meaning representations. A thorough experimental study on Unimer reveals that neural semantic parsing approaches exhibit notably different performance when they are trained to generate different meaning representations. Also, program alias and grammar rules heavily impact the performance of different meaning representations. Our benchmark, execution engines and implementation can be found on: https://github.com/JasperGuo/Unimer.</abstract>
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%0 Conference Proceedings
%T Benchmarking Meaning Representations in Neural Semantic Parsing
%A Guo, Jiaqi
%A Liu, Qian
%A Lou, Jian-Guang
%A Li, Zhenwen
%A Liu, Xueqing
%A Xie, Tao
%A Liu, Ting
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F guo-etal-2020-benchmarking-meaning
%X Meaning representation is an important component of semantic parsing. Although researchers have designed a lot of meaning representations, recent work focuses on only a few of them. Thus, the impact of meaning representation on semantic parsing is less understood. Furthermore, existing work’s performance is often not comprehensively evaluated due to the lack of readily-available execution engines. Upon identifying these gaps, we propose , a new unified benchmark on meaning representations, by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines. The resulting unified benchmark contains the complete enumeration of logical forms and execution engines over three datasets \times four meaning representations. A thorough experimental study on Unimer reveals that neural semantic parsing approaches exhibit notably different performance when they are trained to generate different meaning representations. Also, program alias and grammar rules heavily impact the performance of different meaning representations. Our benchmark, execution engines and implementation can be found on: https://github.com/JasperGuo/Unimer.
%R 10.18653/v1/2020.emnlp-main.118
%U https://aclanthology.org/2020.emnlp-main.118
%U https://doi.org/10.18653/v1/2020.emnlp-main.118
%P 1520-1540
Markdown (Informal)
[Benchmarking Meaning Representations in Neural Semantic Parsing](https://aclanthology.org/2020.emnlp-main.118) (Guo et al., EMNLP 2020)
ACL
- Jiaqi Guo, Qian Liu, Jian-Guang Lou, Zhenwen Li, Xueqing Liu, Tao Xie, and Ting Liu. 2020. Benchmarking Meaning Representations in Neural Semantic Parsing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1520–1540, Online. Association for Computational Linguistics.