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This research introduces Rule-to-Tag (R2T), a framework that integrates linguistic rules as differentiable loss terms to train sequence taggers without labeled data. By formalizing this approach under "principled learning," the method achieves performance comparable to supervised models on low-resource languages like Zarma and Bambara, demonstrating significant potential for NLP in under-resourced contexts.
R2T: Rule-Encoded Loss Functions for Sequence Tagging in Low-Resource Languages
arXiv:2510.13854v2 Announce Type: replace-cross Abstract: We introduce Rule-to-Tag (R2T), a framework that turns linguistic rules into the training signal for neural sequence taggers in low-resource languages. R2T encodes lexical, morphological, and syntactic rules as differentiable loss terms, so that a tagger learns from rules and unlabeled text, with no labeled training data. It also adds an out-of-vocabulary (OOV) loss term that discourages confident predictions on words that no rule covers. R2T is a first instance of a broader paradigm we call principled learning (PrL): using explicit principles as a learning signal, rather than relying on example-based supervision alone. We evaluate R2T on part-of-speech (POS) tagging for Zarma (Songhay), Bambara (Mande), and French (Romance), and on named entity recognition (NER) for Zarma. On Zarma POS tagging, R2T-BiLSTM uses no labeled training data, yet it reaches 0.968 Macro F1. This score comes within 0.007 of a supervised BiLSTM-CRF trained on 300 labeled sentences (0.975) and exceeds AfriBERTa fine-tuned on the same sentences (0.941). On NER, R2T works well as pre-training: after R2T pre-training, a model fine-tuned on 50 labeled sentences outperforms AfriBERTa fine-tuned on 300 (0.83 vs.\ 0.79 span F1). On Bambara, R2T reaches 0.91 Macro F1 with rules written in about 2.75 hours, while a supervised Masakhane tagger reaches 0.78. We release ZarmaPOS-Bench, a silver-standard Zarma POS corpus, together with ZarmaNER-600 and all trained models, to support future work on under-resourced languages.
Abstract:We introduce Rule-to-Tag (R2T), a framework that turns linguistic rules into the training signal for neural sequence taggers in low-resource languages. R2T encodes lexical, morphological, and syntactic rules as differentiable loss terms, so that a tagger learns from rules and unlabeled text, with no labeled training data. It also adds an out-of-vocabulary (OOV) loss term that discourages confident predictions on words that no rule covers. R2T is a first instance of a broader paradigm we call principled learning (PrL): using explicit principles as a learning signal, rather than relying on example-based supervision alone. We evaluate R2T on part-of-speech (POS) tagging for Zarma (Songhay), Bambara (Mande), and French (Romance), and on named entity recognition (NER) for Zarma. On Zarma POS tagging, R2T-BiLSTM uses no labeled training data, yet it reaches 0.968 Macro F1. This score comes within 0.007 of a supervised BiLSTM-CRF trained on 300 labeled sentences (0.975) and exceeds AfriBERTa fine-tuned on the same sentences (0.941). On NER, R2T works well as pre-training: after R2T pre-training, a model fine-tuned on 50 labeled sentences outperforms AfriBERTa fine-tuned on 300 (0.83 vs.\ 0.79 span F1). On Bambara, R2T reaches 0.91 Macro F1 with rules written in about 2.75 hours, while a supervised Masakhane tagger reaches 0.78. We release ZarmaPOS-Bench, a silver-standard Zarma POS corpus, together with ZarmaNER-600 and all trained models, to support future work on under-resourced languages.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2510.13854 [cs.CL]
(or arXiv:2510.13854v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2510.13854 Focus to learn more arXiv-issued DOI via DataCite
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