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VietMix: A Naturally-Occurring Parallel Corpus and Augmentation Framework for Vietnamese-English Code-Mixed Machine Translation

Paper · 2026 · Hieu Tran, Phuong-Anh Nguyen-Le, Huy Nghiem et al.

Summary

Machine translation systems handle code-mixed text poorly, especially when Vietnamese and English blend within one sentence on social media. No natural parallel data existed for this pair. Building such data is hard for three reasons: many tokens are ambiguous between the languages (e.g., "ban"), informal Vietnamese often drops diacritics, and English-origin named entities blur language boundaries.

The authors collected Threads.com posts and detected genuine code-mixing in two steps: a high-recall pass with lingua, then a majority vote of three LLMs. After removing PII, they obtained VietMix. Two professional translators produced English references for a held-out test set, with cross-evaluation and expert adjudication.

They also built a three-stage augmentation pipeline. A Qwen2.5-7B En→Vi model iteratively turns English corpora into synthetic code-mixed Vietnamese, and quality filtering follows. Filtered synthetic data outperforms back-translation and zero-shot baselines, while unfiltered data hurts performance. The resource and method provide a reusable template for other low-resource code-mixed settings.

Key points

  • VietMix contains 10,254 code-mixed posts from Threads.com (Oct 2024–Feb 2025), split into train (8,791), dev (462, source-only) and test (1,002, expert-translated).
  • The LID pipeline (lingua plus a GPT-4o / Claude-3.5 / Gemini-1.5 vote) reached 87.98% F1 and 89.75% accuracy on a 400-sample manually annotated set.
  • Test translation used an adapted MQM-Core framework for quality control, and translators covered the Northern, Central and Southern dialects.
  • Augmented models gained up to +3.5 xCOMET over back-translation baselines and up to +11.9 xCOMET over zero-shot models. Without QA filtering, synthetic data fell below the seed-only baseline.
  • The test set is released for non-commercial research use only (CC BY-NC-SA 4.0 / Data Use Agreement), with code at github.com/umd-vietnlp/code-switching. Only the Vi→En direction was evaluated.

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 Maryland, College ParkCollege Park · United States132 works · 25,380 citations🎓Harvard UniversityCambridge · United States112 works · 42,023 citations

 Fields, topics and concepts

📚 Shelves 4The 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 works

🧭 Topics 3Research subjects and languages the work is about.Machine translation · 1,087 worksEnglish · 2,263 worksMultilingual & cross-lingual NLP · 828 works

Cite

Cite as Hieu Tran, Phuong-Anh Nguyen-Le, Huy Nghiem, Quang-Nhan Nguyen, Wei Ai and Marine Carpuat. 2026. VietMix: A Naturally-Occurring Parallel Corpus and Augmentation Framework for Vietnamese-English Code-Mixed Machine Translation. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.eacl-long.342

@inproceedings{tran2026vietmix,
  title = {{VietMix: A Naturally-Occurring Parallel Corpus and Augmentation Framework for Vietnamese-English Code-Mixed Machine Translation}},
  author = {Hieu Tran, Phuong-Anh Nguyen-Le, Huy Nghiem, Quang-Nhan Nguyen, Wei Ai and Marine Carpuat},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.342},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.342}
}

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