In recent years, the rapid development of web and mobile Internet has enriched constant progress and innovation of technology and application in the field of artificial intelligence. More and more artificial intelligence systems and products were introduced to our life. As a typical application of artificial intelligence, human-computer dialogue system is a human-computer interaction system in which machine serves as a cognitive subject. It has wide applicability, high level of convenience, and highly fit in the form of the Turing test.
With the rapid development of computer hardware and software technology and mobile Internet, cognitive human-machine dialogue system with ability to deal effectively with the accurate information interaction, and accord with human nature interaction habits attracts much attention. Just as apple's Siri, Microsoft's Cortana, Google's Now, Baidu's secret personal assistant system, Rokid home robot and Echo smart home service, different forms of dialog system and other various forms of dialogue system has brought more convenient, intelligence, and change for daily life.
This thesis builds the model in view of a practical scene in the application of present dialogue systems, conducts a research towards intelligent reasoning dialog system in specific domain to explore the academic frontier, improve existing methods, and promote the practical application in AI.
After in-depth research of the existing academic research front and careful consideration the adaptability of this task, this paper has carried out three aspects of work: (1) We propose a model combining lexical and semantic-based features for answer sentence selection and won the 2nd place in the NLPCC 2016 open domain evaluation. (2) We adopt the end-to-end neural networks to conduct the research in problems like short-term memory encoding, reasoning and information extraction in dialog history. This network aims at a model without human participation characteristics and rules of design cases as far as possible, activating related semantic information in the memory units by the user's text input, and decode dynamically to produce the response text. (3) Under the framework of this network, we proposes a hierarchical memory networks to explore a key problem in dialog system about the selection of unknown words. Experiments and comparation of existing approaches shows good performance of our model.