Abstract
Often questions provided to open-domain question answering systems are ambiguous. Traditional QA systems that provide a single answer are incapable of answering ambiguous questions since the question may be interpreted in several ways and may have multiple distinct answers. In this paper, we address multi-answer retrieval which entails retrieving passages that can capture majority of the diverse answers to the question. We propose a re-ranking based approach using Determinantal point processes utilizing BERT as kernels. Our method jointly considers query-passage relevance and passage-passage correlation to retrieve passages that are both query-relevant and diverse. Results demonstrate that our re-ranking technique outperforms state-of-the-art method on the AmbigQA dataset.- Anthology ID:
- 2022.coling-1.194
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2220–2225
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.194
- DOI:
- Cite (ACL):
- Poojitha Nandigam, Nikhil Rayaprolu, and Manish Shrivastava. 2022. Diverse Multi-Answer Retrieval with Determinantal Point Processes. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2220–2225, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Diverse Multi-Answer Retrieval with Determinantal Point Processes (Nandigam et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.coling-1.194.pdf
- Data
- Natural Questions