@inproceedings{reddy-etal-2023-social,
title = "Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations",
author = "Reddy, Revanth and
Bai, Hao and
Yao, Wentao and
Suresh, Sharath Chandra Etagi and
Ji, Heng and
Zhai, ChengXiang",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.62",
doi = "10.18653/v1/2023.findings-emnlp.62",
pages = "873--885",
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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<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.</abstract>
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%0 Conference Proceedings
%T Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations
%A Reddy, Revanth
%A Bai, Hao
%A Yao, Wentao
%A Suresh, Sharath Chandra Etagi
%A Ji, Heng
%A Zhai, ChengXiang
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F reddy-etal-2023-social
%X 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.
%R 10.18653/v1/2023.findings-emnlp.62
%U https://aclanthology.org/2023.findings-emnlp.62
%U https://doi.org/10.18653/v1/2023.findings-emnlp.62
%P 873-885
Markdown (Informal)
[Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations](https://aclanthology.org/2023.findings-emnlp.62) (Reddy et al., Findings 2023)
ACL