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GoiBERT: Compact Vietnamese Language Models via Two-Stage Knowledge Distillation· GoiBERT:通过两阶段知识蒸馏实现的紧凑越南语模型

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 inference, and named entity recognition, GoiBERT-large achieves the highest average score among non-te

领域:cs.CL作者:Quang Anh Kiet Tran、Nam Anh Tran、Manh Hung Trinh
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