OpinionConv: Conversational Product Search with Grounded Opinions
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Abstract (arXiv)
When searching for products, the opinions of others play an important role in making informed decisions. Subjective experiences about a product can be a valuable source of information. This is also true in sales conversations, where a customer and a sales assistant exchange facts and opinions about products. However, training an AI for such conversations is complicated by the fact that language models do not possess authentic opinions for their lack of real-world experience. We address this problem by leveraging product reviews as a rich source of product opinions to ground conversational AI in true subjective narratives. With OpinionConv, we develop the first conversational AI for simulating sales conversations. To validate the generated conversations, we conduct several user studies showing that the generated opinions are perceived as realistic. Our assessors also confirm the importance of opinions as an informative basis for decision-making.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 43 references that are in the map. First posted on arXiv 8 Aug 2023.
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13 of 43 references.
References · 13
- 2019Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Goodcited by 209
- 2018Decoupling Strategy and Generation in Negotiation Dialoguescited by 162
- 2019AmazonQA: A Review-Based Question Answering Taskcited by 43
- 2021Conversational Question Answering: A Surveycited by 31
- 2015Addressing Complex and Subjective Product-Related Queries with Customer Reviewscited by 29
- 2019A Dynamic Strategy Coach for Effective Negotiationcited by 26
- 2020SubjQA: A Dataset for Subjectivity and Review Comprehensioncited by 24
- 2016Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systemscited by 15
- 2020Improving BERT Performance for Aspect-Based Sentiment Analysiscited by 15
- 2019Conversational Product Search Based on Negative Feedbackcited by 13
- 2021Answering Product-Questions by Utilizing Questions from Other Contextually Similar Productscited by 12
- 2021End-to-End Conversational Search for Online Shopping with Utterance Transfercited by 7
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- 2019Review Conversational Reading Comprehensioncited by 6
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