Learning objectives
The course aims to provide the basic knowledge of computational linguistics by introducing the concepts of linguistics combined with those of computer science and showing the potential of language technologies applied to the translation of both spoken and signed languages. At the end of the course students 1) will have acquired knowledge of computational linguistics; 2) will have learned the principles and techniques underlying machine and assisted translation systems; 3) will be updated on the latest technologies related to machine translation of signed languages.
Prerequisites
No specific prior computer skills are required.
Course unit content
The topics covered will be the following:
- Introduction to computational linguistics
- History, principles and techniques of machine and assisted translation
- Language technologies for signed languages
- State of the art in the field of automatic translation of signed languages, potential and limitations in the use of avatars
Full programme
Bibliography
In addition to the slides presented in class and the materials that will be uploaded to the ELLY platform, the mandatory text is:
Monti J. (2019). Dalla Zairja alla traduzione automatica. Riflessioni sulla traduzione nell'era digitale. Paolo Loffredo Editore.
Suggested additional book: Jezek E. e Sprugnoli R. (2023). Linguistica Computazionale. Introduzione all'analisi automatica dei testi. Il Mulino. (chapters 1 and 3).
Teaching methods
Lessons in which theoretical parts and practical laboratory activities with the computer are combined.
Assessment methods and criteria
Oral exam with questions on the course program which will include the topics covered in the slides and in the book in the bibliography.
Final evaluation is here described more in detail:
– a fail depends on a lack of knowledge of the basic fundamental contents, a lack of technical vocabulary and a lack of autonomous judgement, analysis, argumentation and communication of the contents;
– scores 18–23/30 reveal minimal performance;
– scores 24–25/30 reveal an adequate performance;
– scores 28–30/30 reveal a very good performance and 30/30 cum laude reveals an excellent performance.
Other information
Attendance is strongly recommended. There is no difference in program between attending and non-attending students.
2030 agenda goals for sustainable development
