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Pre-trained multilingual language models have enabled significantadvancements in cross-lingual transfer. However, these models often exhibit aperformance disparity when transferring from high-resource languages tolow-resource languages, especially for languages that are underrepresented ornot in the pre-training data. Motivated by the superior performance of thesemodels on high-resource languages compared to low-resource languages, weintroduce a Translation-and-fusion framework, which translates low-resourcelanguage text into a high-resource language for annotation using fullysupervised models before fusing the annotations back into the low-resourcelanguage. Based on this framework, we present TransFusion, a model trained tofuse predictions from a high-resource language to make robust predictions onlow-resource languages. We evaluate our methods on two low-resource namedentity recognition (NER) datasets, MasakhaNER2.0 and LORELEI NER, covering 25languages, and show consistent improvement up to +16 F$_1$ over Englishfine-tuning systems, achieving state-of-the-art performance compared toTranslate-train systems. Our analysis depicts the unique advantages of theTransFusion method which is robust to translation errors and source languageprediction errors, and complimentary to adapted multilingual language models.

Better Low-Resource Entity Recognition Through Translation and Annotation Fusion