@inproceedings{li-haffari-2023-active,
title = "Active Learning for Multilingual Semantic Parser",
author = "Li, Zhuang and
Haffari, Gholamreza",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.47",
doi = "10.18653/v1/2023.findings-eacl.47",
pages = "633--639",
abstract = "Current multilingual semantic parsing (MSP) datasets are almost all collected by translating the utterances in the existing datasets from the resource-rich language to the target language. However, manual translation is costly. To reduce the translation effort, this paper proposes the first active learning procedure for MSP (AL-MSP). AL-MSP selects only a subset from the existing datasets to be translated. We also propose a novel selection method that prioritizes the examples diversifying the logical form structures with more lexical choices, and a novel hyperparameter tuning method that needs no extra annotation cost. Our experiments show that AL-MSP significantly reduces translation costs with ideal selection methods. Our selection method with proper hyperparameters yields better parsing performance than the other baselines on two multilingual datasets.",
}
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%0 Conference Proceedings
%T Active Learning for Multilingual Semantic Parser
%A Li, Zhuang
%A Haffari, Gholamreza
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F li-haffari-2023-active
%X Current multilingual semantic parsing (MSP) datasets are almost all collected by translating the utterances in the existing datasets from the resource-rich language to the target language. However, manual translation is costly. To reduce the translation effort, this paper proposes the first active learning procedure for MSP (AL-MSP). AL-MSP selects only a subset from the existing datasets to be translated. We also propose a novel selection method that prioritizes the examples diversifying the logical form structures with more lexical choices, and a novel hyperparameter tuning method that needs no extra annotation cost. Our experiments show that AL-MSP significantly reduces translation costs with ideal selection methods. Our selection method with proper hyperparameters yields better parsing performance than the other baselines on two multilingual datasets.
%R 10.18653/v1/2023.findings-eacl.47
%U https://aclanthology.org/2023.findings-eacl.47
%U https://doi.org/10.18653/v1/2023.findings-eacl.47
%P 633-639
Markdown (Informal)
[Active Learning for Multilingual Semantic Parser](https://aclanthology.org/2023.findings-eacl.47) (Li & Haffari, Findings 2023)
ACL
- Zhuang Li and Gholamreza Haffari. 2023. Active Learning for Multilingual Semantic Parser. In Findings of the Association for Computational Linguistics: EACL 2023, pages 633–639, Dubrovnik, Croatia. Association for Computational Linguistics.