Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

Gautier Izacard, Edouard Grave


Abstract
Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how much these models can benefit from retrieving text passages, potentially containing evidence. We obtain state-of-the-art results on the Natural Questions and TriviaQA open benchmarks. Interestingly, we observe that the performance of this method significantly improves when increasing the number of retrieved passages. This is evidence that sequence-to-sequence models offers a flexible framework to efficiently aggregate and combine evidence from multiple passages.
Anthology ID:
2021.eacl-main.74
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
874–880
Language:
URL:
https://aclanthology.org/2021.eacl-main.74
DOI:
10.18653/v1/2021.eacl-main.74
Bibkey:
Cite (ACL):
Gautier Izacard and Edouard Grave. 2021. Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 874–880, Online. Association for Computational Linguistics.
Cite (Informal):
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering (Izacard & Grave, EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.74.pdf
Code
 additional community code
Data
ConditionalQANatural QuestionsSQuADTriviaQA