GoiBERT: Compact Vietnamese Language Models via Two-Stage Knowledge Distillation
Vietnamese pre-trained encoders have improved rapidly, but the strongest ones are large, multilingual-vocabulary models or depend on external word segmentation, which makes them costly to deploy. We present GoiBERT, a family of two compact Vietnamese encoders with 26M and 49M parameters that operate directly on raw text. GoiBERT learns from two complementary teachers. A same-tokenizer Vietnamese encoder first teaches general-purpose representations. A much larger teacher then provides task-level guidance through soft labels that are generated without ever labeling its own training data. Across eight benchmarks covering text classification, aspect-based sentiment analysis, natural language...
| Authors | Quang Anh Kiet Tran, Nam Anh Tran, Manh Hung Trinh et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 October 2026 |
| Last updated at source | 21 October 2026 |
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