AI RESEARCH
Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages
arXiv CS.LG
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ArXi:2605.02608v1 Announce Type: cross Transformer-based models achieve state-of-the-art dependency parsing for high-resource languages, yet their advantage over simpler architectures in low-resource settings remains poorly understood. We evaluate four parsers -- the Biaffine LSTM, Stack-Pointer Network, AfroXLMR-large, and RemBERT -- across ten typologically diverse languages, with a focus on low-resource African languages. We find that the Biaffine LSTM consistently outperforms transformer models in low-resource regimes, with transformers recovering their advantage as.