  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.
