CoRE-CoG: Conversational Recommendation of Entities using Constrained Generation

• URL: http://arxiv.org/abs/2311.08511v1
• Date: Tue, 14 Nov 2023 20:07:34 GMT
• Title: CoRE-CoG: Conversational Recommendation of Entities using Constrained Generation
• Authors: Harshvardhan Srivastava and Kanav Pruthi and Soumen Chakrabarti and Mausam
• Abstract summary: End-to-end conversational recommendation systems generate responses by leveraging both dialog history and a knowledge base (KB) A CRS mainly faces three key challenges: (1) at each turn, it must decide if recommending a KB entity is appropriate; if so, it must identify the most relevant KB entity to recommend; and finally, it must recommend the entity in a fluent utterance that is consistent with the conversation history. CoRE-CoG addresses the limitations in prior systems by implementing a recommendation trigger that decides if the system utterance should include an entity, a type pruning module that improves the relevance of recommended entities, and a novel constrained response
• Score: 46.05111252227757
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: End-to-end conversational recommendation systems (CRS) generate responses by leveraging both dialog history and a knowledge base (KB). A CRS mainly faces three key challenges: (1) at each turn, it must decide if recommending a KB entity is appropriate; if so, it must identify the most relevant KB entity to recommend; and finally, it must recommend the entity in a fluent utterance that is consistent with the conversation history. Recent CRSs do not pay sufficient attention to these desiderata, often generating unfluent responses or not recommending (relevant) entities at the right turn. We introduce a new CRS we call CoRE-CoG. CoRE-CoG addresses the limitations in prior systems by implementing (1) a recommendation trigger that decides if the system utterance should include an entity, (2) a type pruning module that improves the relevance of recommended entities, and (3) a novel constrained response generator to make recommendations while maintaining fluency. Together, these modules ensure simultaneous accurate recommendation decisions and fluent system utterances. Experiments with recent benchmarks show the superiority particularly on conditional generation sub-tasks with close to 10 F1 and 4 Recall@1 percent points gain over baselines.

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