With the development of deep learning, the method of large-scale dialogue generation based on deep learning has received extensive attention. The current research has aimed to solve the problem of the quality of generated dialogue content, but has failed to fully consider the emotional factors of generated dialogue content. In order to solve the problem of emotional response in the open domain dialogue system, we proposed a dynamic emotional session generation model (DESG). On the basis of the Seq2Seq (sequence-to-sequence) framework, the model abbreviation incorporates a dictionary-based attention mechanism that encourages the substitution of words in response with synonyms in emotion dictionaries. Meanwhile, in order to improve the model, internal emotion regulator and emotion classifier mechanisms are introduced in order to build a large-scale emotion-session generation model. Experimental results show that our DESG model can not only produce an appropriate output sequence in terms of content (related grammar) for a given post and emotion category, but can also express the expected emotional response explicitly or implicitly.

Knowledge base question answering (KBQA) aims to analyze the semantics of natural language questions and return accurate answers from the knowledge base (KB). More and more studies have applied knowledge bases to question answering systems, and when using a KB to answer a natural language question, there are some words that imply the tense (e.g., original and previous) and play a limiting role in questions. However, most existing methods for KBQA cannot model a question with implicit temporal constraints. In this work, we propose a model based on a bidirectional attentive memory network, which obtains the temporal information in the question through attention mechanisms and external knowledge. Specifically, we encode the external knowledge as vectors, and use additive attention between the question and external knowledge to obtain the temporal information, then further enhance the question vector to increase the accuracy. On the WebQuestions benchmark, our method not only performs better with the overall data, but also has excellent performance regarding questions with implicit temporal constraints, which are separate from the overall data. As we use attention mechanisms, our method also offers better interpretability.