@inproceedings{afbfa9c92d6e40068bedeef38b175c2e,
title = "Profile consistency identification for open-domain dialogue agents",
abstract = "Maintaining a consistent attribute profile is crucial for dialogue agents to naturally converse with humans. Existing studies on improving attribute consistency mainly explored how to incorporate attribute information in the responses, but few efforts have been made to identify the consistency relations between response and attribute profile. To facilitate the study of profile consistency identification, we create a large-scale human-annotated dataset with over 110K single-turn conversations and their key-value attribute profiles. Explicit relation between response and profile is manually labeled. We also propose a key-value structure information enriched BERT model to identify the profile consistency, and it gained improvements over strong baselines. Further evaluations on downstream tasks demonstrate that the profile consistency identification model is conducive for improving dialogue consistency.",
author = "Haoyu Song and Yan Wang and Zhang, \{Wei Nan\} and Zhengyu Zhao and Ting Liu and Xiaojiang Liu",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics; 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 ; Conference date: 16-11-2020 Through 20-11-2020",
year = "2020",
language = "英语",
series = "EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "6651--6662",
booktitle = "EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",
address = "澳大利亚",
}