CKIP at the NTCIR-13 STC-2 Task

In recent years, LSTM-based sequence-to-sequence model have been applied successfully in many fields, including short text conversation and machine translation. The inputs and outputs of the models are usually word sequences. However, for a fixed-size training corpus, a word sequence or even part of it is unlikely to repeat many times, thus in natural, data sparseness problem could be an obstacle for training of sequence-to-sequence model. To address this issue, through this task, we propose the idea of using LSTM with concept sequence. That is, given input word sequence, we first predict the concept for each word of the word sequence and thus form a concept sequence as the input of the LSTM model. At training phase, the output remains the form of word sequence. So during testing phase, given a generated concept sequence, LSTM model is able to directly output the corresponding response in a form of word sequence. Although our results are not among top systems in this task, the experimental results still show the potential of this idea through the comparison among our submitted runs.