Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots

ACL 2018

Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots

Jan 28, 2021
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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 unla-beled data. Experimental results on two public data sets indicate that matching models get significant improvements when they are learned with the proposed method. Authors: Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou (Beihang University, Microsoft Research)

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