Diverse Multi-Answer Retrieval with Determinantal Point Processes

Autor:	Nandigam, Poojitha, Rayaprolu, Nikhil, Shrivastava, Manish
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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. Comment: Published as a conference paper at COLING 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.16029 Zobrazit plný text záznamu View this record from Arxiv