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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- 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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.194.pdf
- Data
- Natural Questions
Export citation
@inproceedings{nandigam-etal-2022-diverse,
title = "Diverse Multi-Answer Retrieval with Determinantal Point Processes",
author = "Nandigam, Poojitha and
Rayaprolu, Nikhil and
Shrivastava, Manish",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.194",
pages = "2220--2225",
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.",
}
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%0 Conference Proceedings %T Diverse Multi-Answer Retrieval with Determinantal Point Processes %A Nandigam, Poojitha %A Rayaprolu, Nikhil %A Shrivastava, Manish %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F nandigam-etal-2022-diverse %X 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. %U https://aclanthology.org/2022.coling-1.194 %P 2220-2225
Markdown (Informal)
[Diverse Multi-Answer Retrieval with Determinantal Point Processes](https://aclanthology.org/2022.coling-1.194) (Nandigam et al., COLING 2022)
- Diverse Multi-Answer Retrieval with Determinantal Point Processes (Nandigam et al., COLING 2022)
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.