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

TL;DR

This paper introduces VietMix, a new Vietnamese-English code-mixed parallel corpus, and a data augmentation framework that significantly improves translation quality for low-resource, informal language settings.

Contribution

The paper presents VietMix, the first expert-translated parallel corpus for Vietnamese-English code-mixed text, and a novel augmentation pipeline that enhances translation models in low-resource scenarios.

Findings

Models with augmented data outperform back-translation baselines by up to 3.5 xCOMET points.

Zero-shot models improve by up to 11.9 points with the proposed augmentation.

VietMix provides a valuable resource for Vietnamese-English code-mixed machine translation.

Abstract

Machine translation (MT) systems universally degrade when faced with code-mixed text. This problem is more acute for low-resource languages that lack dedicated parallel corpora. This work directly addresses this gap for Vietnamese-English, a language context characterized by challenges including orthographic ambiguity and the frequent omission of diacritics in informal text. We introduce VietMix, the first expert-translated, naturally occurring parallel corpus of Vietnamese-English code-mixed text. We establish VietMix's utility by developing a data augmentation pipeline that leverages iterative fine-tuning and targeted filtering. Experiments show that models augmented with our data outperform strong back-translation baselines by up to +3.5 xCOMET points and improve zero-shot models by up to +11.9 points. Our work delivers a foundational resource for a challenging language pair and provides a validated, transferable framework for building and augmenting corpora in other low-resource settings.