@inproceedings{pan-etal-2019-improving,
    title = "Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration",
    author = "Pan, Zhufeng  and
      Bai, Kun  and
      Wang, Yan  and
      Zhou, Lianqiang  and
      Liu, Xiaojiang",
    editor = "Inui, Kentaro  and
      Jiang, Jing  and
      Ng, Vincent  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/D19-1191/",
    doi = "10.18653/v1/D19-1191",
    pages = "1824--1833",
    abstract = "In multi-turn dialogue, utterances do not always take the full form of sentences. These incomplete utterances will greatly reduce the performance of open-domain dialogue systems. Restoring more incomplete utterances from context could potentially help the systems generate more relevant responses. To facilitate the study of incomplete utterance restoration for open-domain dialogue systems, a large-scale multi-turn dataset Restoration-200K is collected and manually labeled with the explicit relation between an utterance and its context. We also propose a ``pick-and-combine'' model to restore the incomplete utterance from its context. Experimental results demonstrate that the annotated dataset and the proposed approach significantly boost the response quality of both single-turn and multi-turn dialogue systems."
}