@inproceedings{wu-etal-2021-dialki,
    title = "{DIALKI}: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization",
    author = "Wu, Zeqiu  and
      Lu, Bo-Ru  and
      Hajishirzi, Hannaneh  and
      Ostendorf, Mari",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.emnlp-main.140/",
    doi = "10.18653/v1/2021.emnlp-main.140",
    pages = "1852--1863",
    abstract = "Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation. We introduce a knowledge identification model that leverages the document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. An auxiliary loss captures the history of dialogue-document connections. We demonstrate the effectiveness of our model on two document-grounded conversational datasets and provide analyses showing generalization to unseen documents and long dialogue contexts."
}