Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing

Zhenwen Li, Jiaqi Guo, Qian Liu, Jian-Guang Lou, Tao Xie


Abstract
Previous research has shown that the design of Meaning Representation (MR) greatly influences the final model performance of a neural semantic parser. Therefore, designing a good MR is a long-term goal for semantic parsing. However, it is still an art as there is no quantitative indicator that can tell us which MR among a set of candidates may have the best final model performance. In practice, in order toselect an MR design, researchers often have to go through the whole training-testing process for all design candidates, and the process often costs a lot. In this paper, we propose a data-aware metric called ISS (denoting incremental structural stability) of MRs, and demonstrate that ISS is highly correlated with the final performance. The finding shows that ISS can be used as an indicator for MR design to avoid the costly training-testing process.
Anthology ID:
2022.emnlp-main.237
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3616–3625
Language:
URL:
https://aclanthology.org/2022.emnlp-main.237
DOI:
10.18653/v1/2022.emnlp-main.237
Bibkey:
Cite (ACL):
Zhenwen Li, Jiaqi Guo, Qian Liu, Jian-Guang Lou, and Tao Xie. 2022. Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3616–3625, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing (Li et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.237.pdf