關鍵詞：回答時機; 時間間隔; 對話; 深度學習; 長短期記憶模型; 注意力機制; 詞向量; 命名實體;In recent years, the technique of natural language processing is growing mature and expand to the application on real life. Then dialogue system also establish a strong position. You need to segment each word of sentence and figure out if relate to the domain. When recognizing, the system must remember the content. And finally, connect all the relation of words and realize the real question of user’s input. Then make system answer the question at the right time. Everything is fine, but it still has some problem. In most of dialogue system, after user inputting the question, the system will answer right away. In implement, it exists some situation like user will input many times to describe their problem. And customer service staff is just waiting. So it is what the problem, our research need to deal with. We mainly use the dialogue data with words, named entity tags, time interval and so on. Using these features to train the deep learning model and find the appropriate ones. Try LSTM, including the gate to remember the content and being good at coping with sequence, and attention mechanism to get the better result. Then we use the pre-training word embedding to reduce the noisy in data. In the end, out thesis compares many kinds of features, and uses attention mechanism to train the model. Then determine which time of user’s input is fine to answer the question. In the telecom domain dialogue dataset, the model is about 2% higher than the one-input and one-answer situation.
Keywords: Answer Time; Time Interval; Dialogue; Deep Learning; Long-Short Term Memory; Attention Mechanism; Word Embedding; Named Entity
