Surprisingly Simple Adapter Ensembling for Zero-Shot Cross-Lingual Sequence Tagging

Abstract

Adapters are parameter-efficient modules added to pretrained Transformer models that facilitate cross-lingual transfer. Language adapters and task adapters can be separately trained and zero-shot transfer is enabled by pairing the language adapter in the target language with a task adapter trained on a high-resource language. However, there are many languages and dialects for which training language adapters would be difficult. In this work, we present a simple and efficient ensembling technique to transfer task knowledge to unseen target languages for which no language adapters exist. We compute a uniformly-weighted ensemble model over the top language adapters based on how well they perform on the test set of a high-resource language. We outperform the state-of-the-art model for this specific setting on named entity recognition (NER) and part-of-speech tagging (POS), across nine typologically diverse languages with relative performance improvements of up to 29\% and 9\% on NER and POS, respectively, on select target languages.

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