mlsanityData to 14 Oct 2026

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

arXiv 2310.14340 ↗First posted 22 Oct 2023cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 22 Oct 2023.

Builds on

20 of 34 references.

References · 20

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8 more
  1. 2022Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedbackcited by 36
  2. 2021Reason first, then respond: Modular Generation for Knowledge-infused Dialoguecited by 32
  3. 2021Commonsense-Focused Dialogues for Response Generation: An Empirical Studycited by 30
  4. 2018Topic-based Evaluation for Conversational Botscited by 16
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  6. 2018Contextual Topic Modeling For Dialog Systemscited by 7
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  8. 2022Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modelingcited by 2

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Citing papers per year: 2026: 1.

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