@inproceedings{su-etal-2020-moviechats,
title = "{M}ovie{C}hats: Chat like Humans in a Closed Domain",
author = "Su, Hui and
Shen, Xiaoyu and
Xiao, Zhou and
Zhang, Zheng and
Chang, Ernie and
Zhang, Cheng and
Niu, Cheng and
Zhou, Jie",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.535",
doi = "10.18653/v1/2020.emnlp-main.535",
pages = "6605--6619",
abstract = "Being able to perform in-depth chat with humans in a closed domain is a precondition before an open-domain chatbot can be ever claimed. In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots. We propose a unified, readily scalable neural approach which reconciles all subtasks like intent prediction and knowledge retrieval. The model is first pretrained on the huge general-domain data, then finetuned on our corpus. We show this simple neural approach trained on high-quality data is able to outperform commercial systems replying on complex rules. On both the static and interactive tests, we find responses generated by our system exhibits remarkably good engagement and sensibleness close to human-written ones. We further analyze the limits of our work and point out potential directions for future work",
}
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<abstract>Being able to perform in-depth chat with humans in a closed domain is a precondition before an open-domain chatbot can be ever claimed. In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots. We propose a unified, readily scalable neural approach which reconciles all subtasks like intent prediction and knowledge retrieval. The model is first pretrained on the huge general-domain data, then finetuned on our corpus. We show this simple neural approach trained on high-quality data is able to outperform commercial systems replying on complex rules. On both the static and interactive tests, we find responses generated by our system exhibits remarkably good engagement and sensibleness close to human-written ones. We further analyze the limits of our work and point out potential directions for future work</abstract>
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%0 Conference Proceedings
%T MovieChats: Chat like Humans in a Closed Domain
%A Su, Hui
%A Shen, Xiaoyu
%A Xiao, Zhou
%A Zhang, Zheng
%A Chang, Ernie
%A Zhang, Cheng
%A Niu, Cheng
%A Zhou, Jie
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F su-etal-2020-moviechats
%X Being able to perform in-depth chat with humans in a closed domain is a precondition before an open-domain chatbot can be ever claimed. In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots. We propose a unified, readily scalable neural approach which reconciles all subtasks like intent prediction and knowledge retrieval. The model is first pretrained on the huge general-domain data, then finetuned on our corpus. We show this simple neural approach trained on high-quality data is able to outperform commercial systems replying on complex rules. On both the static and interactive tests, we find responses generated by our system exhibits remarkably good engagement and sensibleness close to human-written ones. We further analyze the limits of our work and point out potential directions for future work
%R 10.18653/v1/2020.emnlp-main.535
%U https://aclanthology.org/2020.emnlp-main.535
%U https://doi.org/10.18653/v1/2020.emnlp-main.535
%P 6605-6619
Markdown (Informal)
[MovieChats: Chat like Humans in a Closed Domain](https://aclanthology.org/2020.emnlp-main.535) (Su et al., EMNLP 2020)
ACL
- Hui Su, Xiaoyu Shen, Zhou Xiao, Zheng Zhang, Ernie Chang, Cheng Zhang, Cheng Niu, and Jie Zhou. 2020. MovieChats: Chat like Humans in a Closed Domain. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6605–6619, Online. Association for Computational Linguistics.