Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages
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| Format: | Preprint |
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2026
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| _version_ | 1866915978133110784 |
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| author | Guan, Kevin Buzaaba, Happy Fellbaum, Christiane |
| author_facet | Guan, Kevin Buzaaba, Happy Fellbaum, Christiane |
| contents | 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 training data increases. The crossover falls within a resource range typical of treebanks for under-resourced languages. Morphological complexity (measured via MATTR) emerges as a significant secondary predictor of transformers' relative disadvantage after controlling for corpus size. These results indicate that the Biaffine LSTM may be better suited for syntactic tool development in low-resource regimes until sufficient annotated data is available to leverage the representational capacity of pre-trained transformers. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_02608 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages Guan, Kevin Buzaaba, Happy Fellbaum, Christiane Computation and Language Artificial Intelligence Machine Learning 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 training data increases. The crossover falls within a resource range typical of treebanks for under-resourced languages. Morphological complexity (measured via MATTR) emerges as a significant secondary predictor of transformers' relative disadvantage after controlling for corpus size. These results indicate that the Biaffine LSTM may be better suited for syntactic tool development in low-resource regimes until sufficient annotated data is available to leverage the representational capacity of pre-trained transformers. |
| title | Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2605.02608 |