Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations (2023)

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.

## introduction conversational systems have evolved to include personal assistants, task-oriented bots, and opendomain dialog agents for casual conversations. for these agents to maintain engaging and informative discussions, it is crucial to access external knowledge. holding a knowledge-powered dialog @xcite @xcite typically involves the generation of a search query that can help gather the most relevant information to continue the conversation. while such queries are more obvious when the user explicitly asks for certain information, a.k.a. conversational information seeking @xcite , it is unclear what information should be pursued when users are passive, disengaged, and do not provide 1 code and models are available here: https://github.com/gangiswag/dialog-query-generation ## a novel query generation framework in this section, we present our framework for generating search queries by leveraging commonsense reasoning. our approach consists of three main components: topic tracking to pinpoint the core subject, commonsense-based response generation that relates concepts with the primary topic and provides a latent commonsense directive, and instruction-driven query generation to produce a search query capable of retrieving relevant information that follows the commonsense directive. figure 2 illustrates how these components are integrated, with each step described in detail below. ## conclusion and future work we introduce a novel framework that leverages commonsense to enhance the generation of search queries in internet-powered dialog. our results show that incorporating a commonsense-based directive yields search queries with improved relevance, specificity and appeal, fostering user engagement in otherwise passive conversations. future work will use more intricate social narratives by incorporating user preferences from past conversations, to align commonsense directives towards individual interests.