Model Selection for Cross-Lingual Transfer using a Learned Scoring Function
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Transformers that are pre-trained on multilingual text corpora, such as, mBERT and XLM-RoBERTa, have achieved impressive cross-lingual transfer learning results. In the zero-shot cross-lingual transfer setting, only English training data is assumed, and the fine-tuned model is evaluated on another target language. No target-language validation data is assumed in this setting, however substantial variance has been observed in target language performance between different fine-tuning runs. Prior work has relied on English validation/development data to select among models that are fine-tuned with different learning rates, number of steps and other hyperparameters, often resulting in suboptimal choices. To address this challenge, we propose a meta-learning approach to model selection that uses the fine-tuned model's own internal representations to predict its cross-lingual capabilities. In extensive experiments we find that our approach consistently selects better models than English validation data across five languages and five well-studied NLP tasks, achieving results that are comparable to small amounts of target language development data.
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