@inproceedings{feng-2021-dialdoc,
    title = "{D}ial{D}oc 2021 Shared Task: Goal-Oriented Document-grounded Dialogue Modeling",
    author = "Feng, Song",
    editor = "Feng, Song  and
      Reddy, Siva  and
      Alikhani, Malihe  and
      He, He  and
      Ji, Yangfeng  and
      Iyyer, Mohit  and
      Yu, Zhou",
    booktitle = "Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2021.dialdoc-1.1/",
    doi = "10.18653/v1/2021.dialdoc-1.1",
    pages = "1--7",
    abstract = "We present the results of Shared Task at Workshop DialDoc 2021 that is focused on document-grounded dialogue and conversational question answering. The primary goal of this Shared Task is to build goal-oriented information-seeking conversation systems that can identify the most relevant knowledge in the associated document for generating agent responses in natural language. It includes two subtasks on predicting agent responses: the first subtask is to predict the grounding text span in the given document for next agent response; the second subtask is to generate agent response in natural language given the context. Many submissions outperform baseline significantly. For the first task, the best-performing system achieved 67.1 Exact Match and 76.3 F1. For the second subtask, the best system achieved 41.1 SacreBLEU and highest rank by human evaluation."
}