@inproceedings{wu-etal-2018-learning,
    title = "Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots",
    author = "Wu, Yu  and
      Wu, Wei  and
      Li, Zhoujun  and
      Zhou, Ming",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P18-2067/",
    doi = "10.18653/v1/P18-2067",
    pages = "420--425",
    abstract = "We propose a method that can leverage unlabeled data to learn a matching model for response selection in retrieval-based chatbots. The method employs a sequence-to-sequence architecture (Seq2Seq) model as a weak annotator to judge the matching degree of unlabeled pairs, and then performs learning with both the weak signals and the unlabeled data. Experimental results on two public data sets indicate that matching models get significant improvements when they are learned with the proposed method."
}