@inproceedings{li-etal-2022-exploring-secrets,
title = "Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing",
author = "Li, Zhenwen and
Guo, Jiaqi and
Liu, Qian and
Lou, Jian-Guang and
Xie, Tao",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.237",
doi = "10.18653/v1/2022.emnlp-main.237",
pages = "3616--3625",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing
%A Li, Zhenwen
%A Guo, Jiaqi
%A Liu, Qian
%A Lou, Jian-Guang
%A Xie, Tao
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-etal-2022-exploring-secrets
%X 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.
%R 10.18653/v1/2022.emnlp-main.237
%U https://aclanthology.org/2022.emnlp-main.237
%U https://doi.org/10.18653/v1/2022.emnlp-main.237
%P 3616-3625
Markdown (Informal)
[Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing](https://aclanthology.org/2022.emnlp-main.237) (Li et al., EMNLP 2022)
ACL