@inproceedings{fad464f05ff2428cad7c6d17f692b4dd,
title = "Towards end-to-end learning for efficient dialogue agent by modeling looking-ahead ability",
abstract = "Learning an efficient manager of dialogue agent from data with little manual intervention is important, especially for goal-oriented dialogues. However, existing methods either take too many manual efforts (e.g. reinforcement learning methods) or cannot guarantee the dialogue efficiency (e.g. sequence-to-sequence methods). In this paper, we address this problem by proposing a novel end-to-end learning model to train a dialogue agent that can look ahead for several future turns and generate an optimal response to make the dialogue efficient. Our method is data-driven and does not require too much manual work for intervention during system design. We evaluate our method on two datasets of different scenarios and the experimental results demonstrate the efficiency of our model.",
author = "Zhuoxuan Jiang and Mao, {Xian Ling} and Ziming Huang and Jie Ma and Shaochun Li",
note = "Publisher Copyright: {\textcopyright}2019 Association for Computational Linguistics; 20th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2019 ; Conference date: 11-09-2019 Through 13-09-2019",
year = "2019",
language = "English",
series = "SIGDIAL 2019 - 20th Annual Meeting of the Special Interest Group Discourse Dialogue - Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "133--142",
booktitle = "SIGDIAL 2019 - 20th Annual Meeting of the Special Interest Group Discourse Dialogue - Proceedings of the Conference",
address = "United States",
}