TokAlign++ learns token alignments between LLM vocabularies from monolingual representations to enable faster adaptation, better text compression, and effective token-level distillation across 15 languages with minimal steps.
Extending Multilingual BERT to Low-Resource Languages
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Pith papers citing it
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cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Biaffine LSTM outperforms transformer parsers like AfroXLMR and RemBERT in low-resource dependency parsing, with transformers gaining advantage as data increases and morphological complexity as a secondary predictor.
citing papers explorer
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TokAlign++: Advancing Vocabulary Adaptation via Better Token Alignment
TokAlign++ learns token alignments between LLM vocabularies from monolingual representations to enable faster adaptation, better text compression, and effective token-level distillation across 15 languages with minimal steps.
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Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages
Biaffine LSTM outperforms transformer parsers like AfroXLMR and RemBERT in low-resource dependency parsing, with transformers gaining advantage as data increases and morphological complexity as a secondary predictor.