Infopaket

Question-answering system

ДВС.1.08

Вибіркова дисципліна для ОП

Second

2022/2023

4 Semester

3

PLO 11. To perform a scholarly analysis of linguistic, speech, and literary material, interpret and structure it considering expedient methodological principles, and formulate summarizations based on the individually processed data. PLO 16. To use specialized conceptual knowledge in applied linguistics to solve complex tasks and issues, which requires updates and integration of knowledge, often under conditions of incomplete/lacking information and contradictory requirements. PLO 17. To plan, organize, perform, and present the research and or innovative developments in an applied linguistics field. PLO 20.1. To apply the methods of designing dialogue systems (question-answer system), systems of speech recognition and synthesis, automatic editing and text retrieval, content analysis, and sentiment analysis in the creation of linguistic information technologies. PLO 22.1.

Full-time form

1) Successful passing of the courses: Methodology and Organization of scientific research with the basics of intellectual property, Main directions of applied linguistics, Synthesis and recognition of spoken speech, Current issues of data analysis (Data mining), Information search, and obtaining information from the text (Text-mining), Software engineering, Machine learning models and methods; 2) Knowledge of the theoretical foundations of general linguistics, mathematical and computer linguistics, computer science, and programming; 3) Mastering the skills of mathematical and computer modeling of linguistic phenomena; mathematical basics of computer technology; 4) Ability to create linguistic databases and knowledge; use a programming language to model linguistic phenomena.

Within the discipline, different types of dialogue systems, their architecture, and main components are considered; modern approaches, technologies, and methods of designing and developing dialog systems and their separate components using modern computer tools and tools; methods of evaluating the effectiveness of dialogue systems, approaches to the formation of databases and knowledge for their creation are studied. In practical classes, students will be able to work on the main components of modern dialogue systems (including the speech recognition unit, the semantic interpretation of input data, dialogue managers, dialogue architecture, and voice synthesis of output data) and databases, and knowledge necessary for building dialogue systems.

J. Gao et al. Neural Approaches to Conversational AI. Springer. 2016. Goldberg Y. Neural Network Methods for Natural Language Processing. Morgan & Claypool Publishers. 2017. 309 р. S. Janarthanam. Hands-On Chatbots and Conversational UI Development. Packt Publishing. 2017. 394 p. Jokinen K., McTear M. Spoken Dialogue Systems. Morgan & Claypool Publishers, 2010. D. Jurafsky, J. H. Martin. Speech and Language Processing. 2021. https://web.stanford.edu/~jurafsky/slp3/

Lectures, laboratory, and practical classes, independent work. Types of work: The answer is in the practical lesson. Laboratory. Preparation and presentation of the report. Tests.

During the semester, evaluation is carried out according to the types of work and the form of control (oral answers, laboratory work, presentation of a scientific report, tests). The credit is given based on the results of the student's work throughout the entire semester and does not include additional assessment measures. Students who scored the minimum positive number of points (60), receive "Passed". Students who did not score the minimum positive number of points (60), receive a "Failed".

Ukrainian, English