Robust Example-based Dialog Retrieval using Distributed Word Representations and Recursive Autoencoders

Abstract

Based on our previous work on example-based chat-oriented dialog systems that utilize a human-to-human conversation. Though promising, our previous simple retrieval techniques resulting a weakness on handling an out of vocabulary (OOV) database queries. In this paper we discuss an approach to increase the robustness of example-based dialog response retrieval. We employ a recursive neural network paraphrase identification technique to achieve a good performance on finding a good response in dialog-pair database. To achieve that, we remodel our previous dialog-pair database into distributed word representation. We apply an recursive autoencoders and dynamic pooling algorithm between user utterance and database to decide whether they have a same meaning or not, even when they composed in different word and structure. These distribute representations and recusive autoencoders have the potential to enhance our retrieval techniques and reduce confusion in example matching, especially when handling an OOV cases. We also present the system performance evaluation based on objective and subjective metrics.