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The paper introduces CWoMP, a contrastive word-morpheme pretraining method for automated interlinear glossed text (IGT) generation that explicitly models morphemes as atomic form-meaning units. CWoMP uses a contrastively trained encoder to align words with their morphemes in a shared embedding space, and an autoregressive decoder retrieves morphemes from a mutable lexicon. Experiments on low-resource languages demonstrate that CWoMP outperforms existing methods in accuracy and efficiency, especially in extremely low-resource scenarios, while also providing interpretable predictions.
Unlock faster, more accurate interlinear glossing for low-resource languages by treating morphemes as atomic units, outperforming existing methods and enabling user-guided lexicon expansion without retraining.
Interlinear glossed text (IGT) is a standard notation for language documentation which is linguistically rich but laborious to produce manually. Recent automated IGT methods treat glosses as character sequences, neglecting their compositional structure. We propose CWoMP (Contrastive Word-Morpheme Pretraining), which instead treats morphemes as atomic form-meaning units with learned representations. A contrastively trained encoder aligns words-in-context with their constituent morphemes in a shared embedding space; an autoregressive decoder then generates the morpheme sequence by retrieving entries from a mutable lexicon of these embeddings. Predictions are interpretable--grounded in lexicon entries--and users can improve results at inference time by expanding the lexicon without retraining. We evaluate on diverse low-resource languages, showing that CWoMP outperforms existing methods while being significantly more efficient, with particularly strong gains in extremely low-resource settings.
