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.
Learning Which Features Matter: R o BERT a Acquires a Preference for Linguistic Generalizations (Eventually)
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Lil-Bevo applies music pretraining, curriculum learning on sequence length, and targeted masking to small LMs in the BabyLM challenge, finding modest gains from short sequences but overall limited performance.
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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.
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Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways
Lil-Bevo applies music pretraining, curriculum learning on sequence length, and targeted masking to small LMs in the BabyLM challenge, finding modest gains from short sequences but overall limited performance.