Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations

Revanth Reddy, Hao Bai, Wentao Yao, Sharath Chandra Etagi Suresh, Heng Ji, ChengXiang Zhai

Abstract
Open-domain dialog involves generating search queries that help obtain relevant knowledge for holding informative conversations. However, it can be challenging to determine what information to retrieve when the user is passive and does not express a clear need or request. To tackle this issue, we present a novel approach that focuses on generating internet search queries that are guided by social commonsense. Specifically, we leverage a commonsense dialog system to establish connections related to the conversation topic, which subsequently guides our query generation. Our proposed framework addresses passive user interactions by integrating topic tracking, commonsense response generation and instruction-driven query generation. Through extensive evaluations, we show that our approach overcomes limitations of existing query generation techniques that rely solely on explicit dialog information, and produces search queries that are more relevant, specific, and compelling, ultimately resulting in more engaging responses.
Anthology ID:
2023.findings-emnlp.62
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
873–885
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.62
DOI:
10.18653/v1/2023.findings-emnlp.62
Bibkey:
Cite (ACL):
Revanth Reddy, Hao Bai, Wentao Yao, Sharath Chandra Etagi Suresh, Heng Ji, and ChengXiang Zhai. 2023. Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 873–885, Singapore. Association for Computational Linguistics.
Cite (Informal):
Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations (Reddy et al., Findings 2023)
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