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In this paper, we propose a novel end-to-end neural architecture for rankingcandidate answers, that adapts a hierarchical recurrent neural network and alatent topic clustering module. With our proposed model, a text is encoded to avector representation from an word-level to a chunk-level to effectivelycapture the entire meaning. In particular, by adapting the hierarchicalstructure, our model shows very small performance degradations in longer textcomprehension while other state-of-the-art recurrent neural network modelssuffer from it. Additionally, the latent topic clustering module extractssemantic information from target samples. This clustering module is useful forany text related tasks by allowing each data sample to find its nearest topiccluster, thus helping the neural network model analyze the entire data. Weevaluate our models on the Ubuntu Dialogue Corpus and consumer electronicdomain question answering dataset, which is related to Samsung products. Theproposed model shows state-of-the-art results for ranking question-answerpairs.

Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering