Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan

arXiv:2606.09767 · cs.CL, cs.AI, cs.LG · Submitted 2026-06-08 · Read on arXiv

cs.CL, cs.AI, cs.LG

Submitted: 2026-06-08

Updated: 2026-06-08

Comments: Accepted to the 29th International Conference on Text, Speech and Dialogue (TSD 2026). This version of the contribution has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections

Journal ref: Text, Speech, and Dialogue (TSD 2026), Lecture Notes in Computer Science, vol. 16940, pp. 188-200, Springer, 2027

DOI: 10.1007/978-3-032-37249-9_16

Code: https://github.com/achulzhanov/mayan-mt5

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fine-Tuning — 12× in this paper · explained in 5 episodes
• Ablation — 10× in this paper · explained in 1 episode
• Ablation study — 8× in this paper · explained in 1 episode
• Parameter-Efficient Fine-Tuning — 8× in this paper · explained in 2 episodes
• Multi-Task Learning — 7× in this paper
• Transformer — 7× in this paper · explained in 2 episodes
• Data Sovereignty — 6× in this paper · explained in 2 episodes
• Grounding — 6× in this paper · explained in 3 episodes
• Semantic Gap — 6× in this paper · explained in 2 episodes
• Synthetic Data Generation — 6× in this paper · explained in 5 episodes
• LoRA Adapters — 5× in this paper · explained in 3 episodes
• Hallucination — 4× in this paper · explained in 3 episodes
• Neural Machine Translation — 4× in this paper
• Alignment — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning (PEFT) — 2× in this paper · explained in 4 episodes
• Scaffolding — 2× in this paper · explained in 3 episodes
• Algorithmic Bias — 1× in this paper · explained in 1 episode
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Cross-lingual Transfer — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Homogenization — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Translation Edit Rate — 1× in this paper
• alpha — 1× in this paper · explained in 1 episode

Sources

• mT5: A massively multilingual pre-trained text-to-text transformer
• R2T: Rule-Encoded Loss Functions for Sequence Tagging in Low-Resource Languages

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