TUA1 at NTCIR-13 Short Text Conversation 2 Task

In this paper, we describe the overview of our work in Short Text Conversation 2 task at NTCIR-13. We propose two different methods including retrieval-based method and generation-based method. Our retrieval-based method contains index part and reranking part. Rep-post is used as query to search comments from rep-cmnt, and indexed candidate comments are re-ranked by three models respectively. Our generation-based method constructs a sequence-to-sequence neural network model with attention mechanism, to sequentially read a post sentence word by word, calculate an attention weight over the input words, and output a comment sentence with the normal search and the beam search strategies. We propose an RNN model to reorder the generated comment sentences from 26 parallel sequence-to-sequence models by evaluating the fitness between post-comment pairs, and employ a cosine similarity between the post-comment pair to assist the reordering. The evaluation over our groups of Formal Run submissions results suggest that our method is effective for re-ranking and generating a list of meaningful comment sentences for short text conversation.