Open-Domain Question Answering Goes Conversational via Question Rewriting

Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, Srinivas Chappidi


Abstract
We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find answers to conversational questions within a collection of 10M web pages (split into 54M passages). Answers to questions in the same conversation may be distributed across several web pages. QReCC provides annotations that allow us to train and evaluate individual subtasks of question rewriting, passage retrieval and reading comprehension required for the end-to-end conversational question answering (QA) task. We report the effectiveness of a strong baseline approach that combines the state-of-the-art model for question rewriting, and competitive models for open-domain QA. Our results set the first baseline for the QReCC dataset with F1 of 19.10, compared to the human upper bound of 75.45, indicating the difficulty of the setup and a large room for improvement.
Anthology ID:
2021.naacl-main.44
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
520–534
Language:
URL:
https://aclanthology.org/2021.naacl-main.44
DOI:
10.18653/v1/2021.naacl-main.44
Bibkey:
Cite (ACL):
Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, and Srinivas Chappidi. 2021. Open-Domain Question Answering Goes Conversational via Question Rewriting. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 520–534, Online. Association for Computational Linguistics.
Cite (Informal):
Open-Domain Question Answering Goes Conversational via Question Rewriting (Anantha et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.44.pdf
Video:
 https://aclanthology.org/2021.naacl-main.44.mp4
Code
 apple/ml-qrecc
Data
QReCCCANARDMS MARCONatural QuestionsQuAC