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Language Technology Initiative: Framework for Teaching NLP and Computational Linguistics at the Universities in Latvia

Paper · 2026 · Inguna Skadina, Jana Kuzmina, Marina Platonova et al.

Summary

Latvian universities have taught language technology (LT) for about two decades. Recent progress in AI has raised demand for these skills, and the demand now extends past computer science into the humanities and social sciences. In 2022 the Latvian Ministry of Education and Science called for more LT and computational linguistics specialists, and the Language Technology Initiative (LTI) project was set up in response. This short paper presents the courses LTI developed across three institutions: Riga Technical University (RTU), the University of Latvia (UL), and Riga Stradiņš University (RSU). The offer consists of eight modules plus a discourse analysis course, with competences aligned to the DigComp 2.2 framework.

The audiences differ by institution:

  • UL: computer science and humanities students.
  • RTU: interdisciplinary MOOCs.
  • RSU: corpus-based discourse analysis for social scientists.

Teaching started in Spring 2024. Within three semesters, more than 750 students had completed at least one course. External ICT industry experts validated the framework, and students rated it well. The courses are now part of regular curricula and will continue after LTI ends. For other less-resourced languages, the project offers a model for broadening NLP education across disciplines.

Key points

  • UL Faculty of Science and Technology:
    • Bachelor NLP module with 3 courses; its NLP course builds on Jurafsky and Martin, adapted for Latvian.
    • Master NLP module with 2 courses.
    • Jupyter notebooks are published on GitHub (LUMII-AILab/NLP_Course).
  • UL Faculty of Humanities: four modules covering Bachelor, Master (English Studies; Latvian Language, Literature and Culture) and Doctoral levels. Topics include corpus linguistics, lexicography, a Latvian morpheme database and spoken data processing.
  • RTU: two modules, each made of three MOOCs, with at least 50 interactive videos per course and H5P activities.
    • "Language Technologies for Multimodal Information Processing" covers semantics, semiotics and sentiment analysis, using TensorFlow, PyTorch and Hugging Face Transformers.
    • "Language Technologies for Translation Theory and Practice" is the second module.
  • RSU: a Discourse Analysis course in which students:
    • use the Latvian National Corpus Collection, NoSketch Engine and Bash;
    • study contested concepts and ambiguous grammatical forms (AGFs);
    • contribute definitions to the MLVV dictionary.
  • Scale and funding: more than 750 students over three semesters since Spring 2024. LTI is funded by the EU Recovery and Resilience Mechanism and runs until June 2026.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of LatviaRiga · Latvia5 works · 47 citations

Also on the byline: Riga Technical University.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 worksComputer Science Applications · 102 works

Cite

Cite as Inguna Skadina, Jana Kuzmina, Marina Platonova, Tatjana Smirnova and Sergei Kruk. 2026. Language Technology Initiative: Framework for Teaching NLP and Computational Linguistics at the Universities in Latvia. Proceedings of the Seventh Workshop on Teaching Natural Language Processing (TeachNLP 2026).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.teachingnlp-1.5

@inproceedings{skadina2026language,
  title = {{Language Technology Initiative: Framework for Teaching NLP and Computational Linguistics at the Universities in Latvia}},
  author = {Inguna Skadina, Jana Kuzmina, Marina Platonova, Tatjana Smirnova and Sergei Kruk},
  booktitle = {Proceedings of the Seventh Workshop on Teaching Natural Language Processing (TeachNLP 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.teachingnlp-1.5},
  url = {https://doi.org/10.18653/v1/2026.teachingnlp-1.5}
}

References

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