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-tosequence 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.
While writing this paper, we also notice a recent work in REF , which extends the teacher-student scheme to the learning of retrievalbased chatbots, where a sequence-to-sequence model is employed as a weak annotator, and the student model is trained by learning from both the weak signals and the unlabeled data.
Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots
ArXiv
ArXiv
Computer Science