@inproceedings{tang-etal-2019-target,
title = "Target-Guided Open-Domain Conversation",
author = "Tang, Jianheng and
Zhao, Tiancheng and
Xiong, Chenyan and
Liang, Xiaodan and
Xing, Eric and
Hu, Zhiting",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1565",
doi = "10.18653/v1/P19-1565",
pages = "5624--5634",
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",
}
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<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</abstract>
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%0 Conference Proceedings
%T Target-Guided Open-Domain Conversation
%A Tang, Jianheng
%A Zhao, Tiancheng
%A Xiong, Chenyan
%A Liang, Xiaodan
%A Xing, Eric
%A Hu, Zhiting
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F tang-etal-2019-target
%X 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
%R 10.18653/v1/P19-1565
%U https://aclanthology.org/P19-1565
%U https://doi.org/10.18653/v1/P19-1565
%P 5624-5634
Markdown (Informal)
[Target-Guided Open-Domain Conversation](https://aclanthology.org/P19-1565) (Tang et al., ACL 2019)
ACL
- Jianheng Tang, Tiancheng Zhao, Chenyan Xiong, Xiaodan Liang, Eric Xing, and Zhiting Hu. 2019. Target-Guided Open-Domain Conversation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5624–5634, Florence, Italy. Association for Computational Linguistics.