Infopaket

Machine Editing and Translation

ДВС.1.09

Вибіркова дисципліна для ОП

Second

2021/2022

4 Semester

2

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

Before starting this course, students should know and systematically apply methods of text analysis, methods of translation and modeling of the applied (linguistic) field, collect raw data for software design; be able to carry out a scientific analysis of language and speech material, interpret and structure it taking into account appropriate methodological principles, and formulate generalizations based on independently processed data; have elementary skills in scientific research and information management; critical attitude to the analyzed phenomena.

The goal is to study the theoretical foundations of automatic editing and translation; formation of skills in building linguistic algorithms to identify error-prone places in the text; Its subject is the study of theoretical linguistic problems of automation of editorial and publishing processes and translation. The discipline is aimed at the practical implementation of automatic detection of error-prone places in the text, provides insight into the optimization of the communicative qualities of the text, and forms computer editing skills.

Bapna, A., & Firat, O. (2019). Exploring massively multilingual, massive neural machine translation. Google AI Blog, October, 11. Dabre, R., Chu, C., & Kunchukuttan, A. (2020). A survey of multilingual neural machine translation. ACM Computing Surveys (CSUR), 53(5), 1-38. Fan, A., Bhosale, S., Schwenk, H., Ma, Z., El-Kishky, A., Goyal, S., ... & Joulin, A. (2021). Beyond English-centric multilingual machine translation. The Journal of Machine Learning Research, 22(1), 4839-4886. Forcada, M. L., Bonev, B. I., Rojas, S. O., Ortiz, J. P., Sánchez, G. R., Martínez, F. S., ... & Tyers, F. M. (2007). Documentation of the open-source shallow-transfer machine translation platform Apertium. URL: https://wiki.apertium.org/w/images/d/d0/Apertium2-documentation.pdf

Lectures, laboratory, and practical classes, independent work.

Throughout the semester, practicals and classes are held on relevant topics, at which evaluation is carried out according to the types of work and the form of the control (oral answer, laboratory work, modular control work). 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 "Failed".

Ukrainian, English