  In this paper, we propose a novel end-to-end neural architecture for ranking
candidate answers, that adapts a hierarchical recurrent neural network and a
latent topic clustering module. With our proposed model, a text is encoded to a
vector representation from an word-level to a chunk-level to effectively
capture the entire meaning. In particular, by adapting the hierarchical
structure, our model shows very small performance degradations in longer text
comprehension while other state-of-the-art recurrent neural network models
suffer from it. Additionally, the latent topic clustering module extracts
semantic information from target samples. This clustering module is useful for
any text related tasks by allowing each data sample to find its nearest topic
cluster, thus helping the neural network model analyze the entire data. We
evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic
domain question answering dataset, which is related to Samsung products. The
proposed model shows state-of-the-art results for ranking question-answer
pairs.
