Joint Passage Ranking for Diverse Multi-Answer Retrieval
Sewon Mina, Kenton Leea b, Ming-Wei Changb c, Kristina Toutanovab c, Hannaneh Hajishirzia c
- aUniversity of Washington
- bGoogle Research
- cGoogle
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Abstract
We study multi-answer retrieval, an underexplored 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.In this paper, we introduce JPR, the first joint passage retrieval model for multi-answer retrieval.JPR makes use of an autoregressive reranker that selects a sequence of passages, each conditioned on previously selected passages.JPR is trained to select passages that cover new answers at each timestep and uses a tree-decoding algorithm to enable flexibility in the degree of diversity.Compared to prior approaches, JPR achieves significantly better answer coverage on three multianswer 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.
TL;DRJPR is introduced, a joint passage retrieval model focusing on reranking that achieves significantly better answer coverage on three multi-answer datasets, and enables larger answer generation models since they need to consider fewer passages. (Semantic Scholar)