Yu.E. Gapanyuk – Ph.D.(Eng.), Associate Professor, Department «Information Processing and Control Systems», Bauman Moscow State Technical University
A.V. Leontiev – Post-graduate Student, Master, Department «Information Processing and Control Systems», Bauman Moscow State Technical University
I.I. Latkin – Post-graduate Student, Master, Department «Information Processing and Control Systems», Bauman Moscow State Technical University
S.V. Chernobrovkin – Post-graduate Student, Master, Department «Information Processing and Control Systems», Bauman Moscow State Technical University
M.A. Belyanova – Undergraduate, Bachelor, Department «Information Processing and Control Systems», Bauman Moscow State Technical University
O.N. Morozenkov – Student, Department «Information Processing and Control Systems», Bauman Moscow State Technical University
Basically, modern dialog systems (chatbots) satisfactorily cope only with the task of answering frequently asked questions. Also, the task of answering user questions based on the knowledge base is actual. Currently, many users are ready to provide their domain data in the form of a denormalized table (a set of denormalized tables). Typical requirements for a dialog system, in this case, are the following: I. Answers to the frequently asked questions; II. Answers to questions from the knowledge base presented in the form of a denormalized table; III. Active dialog with the user. Asking the user counter questions.
The task of answering a user's question is usually solved as a classification problem, where the answers are the target classes, and possible questions are used as features. There are also commercial systems that implement the functionality of answering frequently asked questions using machine learning methods, in particular, Microsoft QnA Maker.
The frequently asked questions answering module is implemented using a combination of measures TF-IDF (with a weight of 0.8) and Doc2Vec (with a weight of 0.2) and also using the cosine distance. But to answer questions about the knowledge base, machine learning methods do not offer such stable solutions. The most promising methods based on recurrent neural networks (LSTM, Seq2Seq) do not allow to answer questions on the knowledge base accurately.
As a model of the knowledge base, a metagraph model is used. It allows using metavertices both as data elements to answer questions and as information elements to implement an active dialog. The history of the user's responses is saved in the user's session. Counter questions are given to the user on the basis of the knowledge base and the history of his answers.
Linguistic preprocessing of user's questions includes tokenization, transliteration, morphological normalization, phonetization, spell-checking and replacement of synonyms.
The information system provides, as far as possible, seamless integration of the frequently asked questions answering module and the knowledge base processing module.
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