Kaisa_2012_3_photo by Veikko Somerpuro

Anmäl dig
1.2.2019 kl. 09:00 - 3.5.2019 kl. 23:59

Tidsschema

I den här delen hittar du kursens tidsschema. Kontrollera eventuella andra tider i beskrivning.

DatumTidPlats
tis 19.3.2019
12:15 - 13:45
fre 22.3.2019
13:15 - 14:45
tis 26.3.2019
12:15 - 13:45
fre 29.3.2019
13:15 - 14:45
tis 2.4.2019
12:15 - 13:45
fre 5.4.2019
13:15 - 14:45
tis 9.4.2019
12:15 - 13:45
fre 12.4.2019
13:15 - 14:45
tis 16.4.2019
12:15 - 13:45
fre 26.4.2019
13:15 - 14:45
tis 30.4.2019
12:15 - 13:45
fre 3.5.2019
13:15 - 14:45

Beskrivning

The course belongs to the MA Programme Linguistic Diversity in the Digital Age.

  • study track: language technology
  • modules: Studies in Language Technology (LDA-T3100), Essentials in Language Technology (LDA-TA500), Comprehensive specialization in Language Technology (LDA-TB500)

This is an optional course.

The course is available to students from other study tracks and degree programmes.

After successfully completing the course, students will be able to:

  • describe the differences between various machine translation paradigms (rule-based, statistical, neural machine translation)
  • explain the basics of neural machine translation and the most common model architectures
  • describe the issues related to machine translation evaluation
  • train, test and evaluate a neural machine translation system including the appropriate pre- and post-processing steps
  • read, understand and present scientific papers on current challenges in machine translation.

Students can take this course in year 1 or 2. The course is offered during the spring term in period 4.

  • History of machine translation and paradigms
  • Parallel corpora, alignment, preprocessing
  • Human and automatic evaluation of machine translation
  • Introduction to neural networks and neural machine translation
  • Common model architectures: sequence-to-sequence models, attention, self-attention
  • Open vocabulary translation
  • Current topics in machine translation: multilingual MT, unsupervised MT, multimodal MT, domain adaptation, etc.
  • Weekly lectures and hands-on assignments
  • Shared-task-based assignment with report
  • Seminar presentation on current topic
  • Philipp Koehn (2012): Statistical machine translation. Cambridge. [Including appendix chapter on neural machine translation]
  • Mikel Forcada (2017): Making sense of neural machine translation. In: Translation Spaces 6/2.
  • Additional material distributed in the course.

Grading follows the standard scale 0 – 5. The following aspects are taken into account in grading:

  • Hands-on assignments (⅓)
  • Shared-task-based assignment with report (⅓)
  • Seminar presentation on current topic (⅓)

Prerequisites:

  • Command-line course or equivalent

Recommended optional studies:

  • Programming for linguists or equivalent (BA level)
  • Mathematics for linguists or equivalent (BA level)
  • Machine learning for linguists or equivalent (BA level)