Active Learning for Multilingual Semantic Parser

Zhuang Li, Gholamreza Haffari


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.
Anthology ID:
2023.findings-eacl.47
Original:
2023.findings-eacl.47v1
Version 2:
2023.findings-eacl.47v2
Volume:
Findings of the Association for Computational Linguistics: EACL 2023
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
633–639
Language:
URL:
https://aclanthology.org/2023.findings-eacl.47
DOI:
10.18653/v1/2023.findings-eacl.47
Bibkey:
Cite (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.
Cite (Informal):
Active Learning for Multilingual Semantic Parser (Li & Haffari, Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-eacl.47.pdf
Video:
 https://aclanthology.org/2023.findings-eacl.47.mp4