Improving the robustness of example-based dialog retrieval using recursive neural network paraphrase identification

Previous work on example-based chat-oriented dialog systems utilizing real human-to-human conversation has shown promising results. However, most previous methods use relatively simple retrieval techniques, resulting in weakness to out of vocabulary (OOV) database queries and inadequate handling of interactions between words in the sentence. To overcome this problem, in this paper we propose a method to utilize recursive neural network paraphrase identification to improve the accuracy and robustness of example-based dialog response retrieval. We model our dialog-pair database and user input query with distributed word representations, and employ recursive autoencoders and dynamic pooling to determine whether two sentences with arbitrary length have the same meaning. The distributed representations have the potential to improve handling of OOV cases, and the recursive structure can reduce confusion in example matching. We evaluate the system performance based on objective and subjective metrics.