@inproceedings{tang2019target-guided,
author = {Tang, Jianheng and Zhao, Tiancheng and Xiong, Chenyan and Liang, Xiaodan and Xing, Eric P. and Hu, Zhiting},
title = {Target-Guided Open-Domain Conversation},
organization = {ACL},
booktitle = {Annual Meeting of the Association for Computational Linguistics (ACL)},
year = {2019},
month = {July},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/target-guided-open-domain-conversation/},
pages = {5624-5634},
}