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
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2023
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 633–639
- Language:
- URL:
- https://aclanthology.org/2023.findings-eacl.47
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2023.findings-eacl.47.pdf