@inproceedings{ma-etal-2020-compare,
    title = "A Compare Aggregate Transformer for Understanding Document-grounded Dialogue",
    author = "Ma, Longxuan  and
      Zhang, Wei-Nan  and
      Sun, Runxin  and
      Liu, Ting",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2020.findings-emnlp.122/",
    doi = "10.18653/v1/2020.findings-emnlp.122",
    pages = "1358--1367",
    abstract = "Unstructured documents serving as external knowledge of the dialogues help to generate more informative responses. Previous research focused on knowledge selection (KS) in the document with dialogue. However, dialogue history that is not related to the current dialogue may introduce noise in the KS processing. In this paper, we propose a Compare Aggregate Transformer (CAT) to jointly denoise the dialogue context and aggregate the document information for response generation. We designed two different comparison mechanisms to reduce noise (before and during decoding). In addition, we propose two metrics for evaluating document utilization efficiency based on word overlap. Experimental results on the CMU{\_}DoG dataset show that the proposed CAT model outperforms the state-of-the-art approach and strong baselines."
}