Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions

Seonjeong Hwang, Yunsu Kim, Gary Geunbae Lee

Abstract
Conversational question answering (CQA) facilitates an incremental and interactive understanding of a given context, but building a CQA system is difficult for many domains due to the problem of data scarcity. In this paper, we introduce a novel method to synthesize data for CQA with various question types, including open-ended, closed-ended, and unanswerable questions. We design a different generation flow for each question type and effectively combine them in a single, shared framework. Moreover, we devise a hierarchical answerability classification (hierarchical AC) module that improves quality of the synthetic data while acquiring unanswerable questions. Manual inspections show that synthetic data generated with our framework have characteristics very similar to those of human-generated conversations. Across four domains, CQA systems trained on our synthetic data indeed show good performance close to the systems trained on human-annotated data.
Anthology ID:
2022.aacl-short.22
Volume:
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2022
Address:
Online only
Editors:
Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
Venues:
AACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
169–177
Language:
URL:
https://aclanthology.org/2022.aacl-short.22
DOI:
10.18653/v1/2022.aacl-short.22
Bibkey:
Cite (ACL):
Seonjeong Hwang, Yunsu Kim, and Gary Geunbae Lee. 2022. Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 169–177, Online only. Association for Computational Linguistics.
Cite (Informal):
Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions (Hwang et al., AACL-IJCNLP 2022)
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