EMNLP 2022main4 citations

FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows

Jianqiao Zhao, Yanyang Li, Wanyu Du, Yangfeng Ji, Dong Yu, Michael Lyu, Liwei Wang

Abstract

Despite recent progress in open-domain dialogue evaluation, how to develop automatic metrics remains an open problem. We explore the potential of dialogue evaluation featuring dialog act information, which was hardly explicitly modeled in previous methods. However, defined at the utterance level in general, dialog act is of coarse granularity, as an utterance can contain multiple segments possessing different functions. Hence, we propose segment act, an extension of dialog act from utterance level to segment level, and crowdsource a large-scale dataset for it. To utilize segment act flows, sequences of segment acts, for evaluation, we develop the first consensus-based dialogue evaluation framework, FlowEval. This framework provides a reference-free approach for dialog evaluation by finding pseudo-references. Extensive experiments against strong baselines on three benchmark datasets demonstrate the effectiveness and other desirable characteristics of our FlowEval, pointing out a potential path for better dialogue evaluation.

BibTeX
@inproceedings{zhao-etal-2022-floweval,
    title = "{F}low{E}val: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows",
    author = "Zhao, Jianqiao  and
      Li, Yanyang  and
      Du, Wanyu  and
      Ji, Yangfeng  and
      Yu, Dong  and
      Lyu, Michael  and
      Wang, Liwei",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.715/",
    doi = "10.18653/v1/2022.emnlp-main.715",
    pages = "10469--10483"
}