@inproceedings{min-etal-2021-joint,
title = "Joint Passage Ranking for Diverse Multi-Answer Retrieval",
author = "Min, Sewon and
Lee, Kenton and
Chang, Ming-Wei and
Toutanova, Kristina and
Hajishirzi, Hannaneh",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.560",
doi = "10.18653/v1/2021.emnlp-main.560",
pages = "6997--7008",
abstract = "We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a given question. This task requires joint modeling of retrieved passages, as models should not repeatedly retrieve passages containing the same answer at the cost of missing a different valid answer. Prior work focusing on single-answer retrieval is limited as it cannot reason about the set of passages jointly. In this paper, we introduce JPR, a joint passage retrieval model focusing on reranking. To model the joint probability of the retrieved passages, JPR makes use of an autoregressive reranker that selects a sequence of passages, equipped with novel training and decoding algorithms. Compared to prior approaches, JPR achieves significantly better answer coverage on three multi-answer datasets. When combined with downstream question answering, the improved retrieval enables larger answer generation models since they need to consider fewer passages, establishing a new state-of-the-art.",
}
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%0 Conference Proceedings
%T Joint Passage Ranking for Diverse Multi-Answer Retrieval
%A Min, Sewon
%A Lee, Kenton
%A Chang, Ming-Wei
%A Toutanova, Kristina
%A Hajishirzi, Hannaneh
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F min-etal-2021-joint
%X We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a given question. This task requires joint modeling of retrieved passages, as models should not repeatedly retrieve passages containing the same answer at the cost of missing a different valid answer. Prior work focusing on single-answer retrieval is limited as it cannot reason about the set of passages jointly. In this paper, we introduce JPR, a joint passage retrieval model focusing on reranking. To model the joint probability of the retrieved passages, JPR makes use of an autoregressive reranker that selects a sequence of passages, equipped with novel training and decoding algorithms. Compared to prior approaches, JPR achieves significantly better answer coverage on three multi-answer datasets. When combined with downstream question answering, the improved retrieval enables larger answer generation models since they need to consider fewer passages, establishing a new state-of-the-art.
%R 10.18653/v1/2021.emnlp-main.560
%U https://aclanthology.org/2021.emnlp-main.560
%U https://doi.org/10.18653/v1/2021.emnlp-main.560
%P 6997-7008
Markdown (Informal)
[Joint Passage Ranking for Diverse Multi-Answer Retrieval](https://aclanthology.org/2021.emnlp-main.560) (Min et al., EMNLP 2021)
ACL
- Sewon Min, Kenton Lee, Ming-Wei Chang, Kristina Toutanova, and Hannaneh Hajishirzi. 2021. Joint Passage Ranking for Diverse Multi-Answer Retrieval. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6997–7008, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.