Denoising Table-Text Retrieval for Open-Domain Question Answering

Deokhyung Kang, Baikjin Jung, Yunsu Kim, Gary Geunbae Lee

Abstract
In table-text open-domain question answering, a retriever system retrieves relevant evidence from tables and text to answer questions. Previous studies in table-text open-domain question answering have two common challenges: firstly, their retrievers can be affected by false-positive labels in training datasets; secondly, they may struggle to provide appropriate evidence for questions that require reasoning across the table. To address these issues, we propose Denoised Table-Text Retriever (DoTTeR). Our approach involves utilizing a denoised training dataset with fewer false positive labels by discarding instances with lower question-relevance scores measured through a false positive detection model. Subsequently, we integrate table-level ranking information into the retriever to assist in finding evidence for questions that demand reasoning across the table. To encode this ranking information, we fine-tune a rank-aware column encoder to identify minimum and maximum values within a column. Experimental results demonstrate that DoTTeR significantly outperforms strong baselines on both retrieval recall and downstream QA tasks. Our code is available at https://github.com/deokhk/DoTTeR.
Anthology ID:
2024.lrec-main.414
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
4634–4640
Language:
URL:
https://aclanthology.org/2024.lrec-main.414
DOI:
Bibkey:
Cite (ACL):
Deokhyung Kang, Baikjin Jung, Yunsu Kim, and Gary Geunbae Lee. 2024. Denoising Table-Text Retrieval for Open-Domain Question Answering. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 4634–4640, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Denoising Table-Text Retrieval for Open-Domain Question Answering (Kang et al., LREC-COLING 2024)
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https://preview.aclanthology.org/nschneid-patch-3/2024.lrec-main.414.pdf
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