Year: 2019
Type: conference-paper
Abstract: Many real-world open-domain conversation applications have specific goals to achieve during open-ended chats, such as recommendation, psychotherapy, education, etc.We study the problem of imposing conversational goals on open-domain chat agents.In particular, we want a conversational system to chat naturally with human and proactively guide the conversation to a designated target subject.The problem is challenging as no public data is available for learning such a target-guided strategy.We propose a structured approach that introduces coarse-grained keywords to control the intended content of system responses.We then attain smooth conversation transition through turn-level supervised learning, and drive the conversation towards the target with discourse-level constraints.We further derive a keyword-augmented conversation dataset for the study.Quantitative and human evaluations show our system can produce meaningful and effective conversations, significantly improving over other approaches 1 .
DOI: 10.18653/v1/p19-1565
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2950299257