Diverse Multi-Answer Retrieval with Determinantal Point Processes

Poojitha Nandigam, Nikhil Rayaprolu, Manish Shrivastava


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:
Bibkey:
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)
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PDF:
https://aclanthology.org/2022.coling-1.194.pdf