- Docente: Luca Astolfi
- Credits: 5
- SSD: ING-INF/05
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Forli
- Corso: Second cycle degree programme (LM) in Specialized translation (cod. 6826)
Learning outcomes
The student knows the history, theoretical principles, and latest developments of machine translation (MT) and artificial intelligence (AI) applied to language and translation services; they understand the principles of pre- and post-editing, as well as prompt design and engineering, and can apply them maintaining adequate ethical standards; they can plan, execute, and evaluate complex projects involving other professionals and a range of AI-based language and translation tools; they are able to acquire further knowledge independently in both MT and AI technologies and apply it to optimize and innovate language industry processes.
Course contents
In this course, the student will learn:
- the main phases of the history and evolution of machine translation
- the intuition behind the main principles underlying MT
- how to apply and interpret the most important evaluation metrics for machine-produced translations
- how to set up and maintain a MT model, including gathering and cleaning training data
- to use specialised MT software and utilities
- to think about MT systems critically and leverage their knowledge to boost productivity
Readings/Bibliography
Philipp Koehn (2020), "Neural Machine Translation", Cambridge University Press
Thierry Poibeau (2017), "Machine Translation", MIT Press
Joss Moorkens, S. Castilho, F. Gaspari, S. Doherty (2018), "Translation Quality Assessment: From Principles to Practice", Springer
Kyunghyun C., van Merrienboer B., Bahdanau D., Bengio Y. (2016) "On the Properties of Neural Machine Translation: Encoder-Decoder Approaches" arXiv.org > cs > arXiv:1409.1259
Teaching methods
Lectures and workshops
Assessment methods
Capstone project followed by a viva voce.
Students with specific learning difficulties (SpLD) or with disabilities that can affect their ability to attend courses are invited to contact the University service for students with disabilities and SLD at the earliest opportunity -- ideally before the start of the course: https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students. The University service will suggest possible adjustments to the course work and/or exam, which must then be submitted to the course leader so they can assess their feasibility, in line with the learning objectives of the course. Please note that adjustments to the exam must be requested at least two weeks in advance.
Teaching tools
PowerPoint files used during lectures will be made available to students. Specific MT software will be used during workshops.
Office hours
See the website of Luca Astolfi