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Dialog state tracking for interview coaching using two-level LSTM

This study presents an approach to dialog state tracking (DST) in an interview conversation by using the long short-term memory (LSTM) and artificial neural network (ANN). First, the techniques of word embedding are employed for word representation by using the word2vec model. Then, each input sentence is represented by a sentence hidden vector using the LSTM-based sentence model. The sentence hidden vectors for each sentence are fed to the LSTM-based answer model to map the interviewee's answer to an answer hidden vector. For dialog state detection, the answer hidden vector is finally used to detect the dialog state using an ANN-based dialog state detection model. To evaluate the proposed method, an interview conversation system was constructed, and an average accuracy of 89.93% was obtained for dialog state detection.