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

• URL: http://arxiv.org/abs/2310.14340v1
• Date: Sun, 22 Oct 2023 16:14:56 GMT
• Title: Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations
• Authors: Revanth Gangi Reddy, Hao Bai, Wentao Yao, Sharath Chandra Etagi Suresh, Heng Ji, ChengXiang Zhai
• Abstract summary: We present a novel approach that focuses on generating internet search queries guided by social commonsense. Our proposed framework addresses passive user interactions by integrating topic tracking, commonsense response generation and instruction-driven query generation.
• Score: 66.16863141262506
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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