Abstract
In this paper we describe how we mimick the different steps in the historical development of NMT systems, all trained and evaluated on the same small data set. We do this for pedagogic reasons so students can see the effect of each of the steps on metrics like BLEU but also on qualitative examples, which the training scripts generate after each epoch. As MT paradigms, we discuss NMT training from scratch, finetuning pretrained encoder-decoder models, and finally prompt engineering for decoder only models. All models run in Kaggle sessions and all Python scripts and JuPyter notebooks are made available to the MT teaching community through Github and public Kaggle sessions.
- Anthology ID:
- 2026.taitt-1.3
- Volume:
- [Proceedings of the 1st International Workshop on Teaching AI-Based Translation and Technologies (TAITT 2026)](https://aclanthology.org/volumes/2026.taitt-1/)
- Month:
- June
- Year:
- 2026
- Address:
- Tilburg, the Netherlands
- Editors:
- Ralph Krüger ,Dorothy Kenny ,Sheila Castilho ,Sergi Álvarez-Vidal ,Nora Aranberri ,María Isabel Rivas Ginel ,Janiça Hackenbuchner
- Venues:
- [TAITT](https://aclanthology.org/venues/taitt/) |[WS](https://aclanthology.org/venues/ws/)
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 19–27
- Language:
- URL:
- [https://aclanthology.org/2026.taitt-1.3/](https://aclanthology.org/2026.taitt-1.3/)
- DOI:
- Cite (ACL):
- Vincent Vandeghinste. 2026. Mimicking Neural Machine Translation History for Pedagogic Reasons . InProceedings of the 1st International Workshop on Teaching AI-Based Translation and Technologies (TAITT 2026) , pages 19–27, Tilburg, the Netherlands. European Association for Machine Translation.
- Cite (Informal):
- [Mimicking Neural Machine Translation History for Pedagogic Reasons](https://aclanthology.org/2026.taitt-1.3/) (Vandeghinste, TAITT 2026)
- PDF:
- [https://aclanthology.org/2026.taitt-1.3.pdf](https://aclanthology.org/2026.taitt-1.3.pdf)
source & further reading
aclanthology.org — original article
A Chinese Challenge Set to Assess Gender Bias in Automated Translation
Beyond post-editing: A project-based module on MT and LLM integration for trainee translators
Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editing