Datasets for Multilingual Answer Sentence Selection
Matteo Gabburo, Stefano Campese, Federico Agostini, Alessandro Moschitti
Abstract
Answer Sentence Selection (AS2) is a critical task for designing effective retrieval-based Question Answering (QA) systems. Most advancements in AS2 focus on English due to the scarcity of annotated datasets for other languages. This lack of resources prevents the training of effective AS2 models in different languages, creating a performance gap between QA systems in English and other locales. In this paper, we introduce new high-quality datasets for AS2 in five European languages (French, German, Italian, Portuguese, and Spanish), obtained through supervised Automatic Machine Translation (AMT) of existing English AS2 datasets such as ASNQ, WikiQA, and TREC-QA using a Large Language Model (LLM). We evaluated our approach and the quality of the translated datasets through multiple experiments with different Transformer architectures. The results indicate that our datasets are pivotal in producing robust and powerful multilingual AS2 models, significantly contributing to closing the performance gap between English and other languages.- Anthology ID:
- 2024.findings-emnlp.522
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8947–8958
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2024.findings-emnlp.522/
- DOI:
- 10.18653/v1/2024.findings-emnlp.522
- Cite (ACL):
- Matteo Gabburo, Stefano Campese, Federico Agostini, and Alessandro Moschitti. 2024. Datasets for Multilingual Answer Sentence Selection. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 8947–8958, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Datasets for Multilingual Answer Sentence Selection (Gabburo et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2024.findings-emnlp.522.pdf