FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding

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We present FILTER, a new approach for cross-lingual language understanding that first encodes paired language input independently, fuses them in the intermediate layers of XLM, and performs further language-specific encoding

Abstract:

Large-scale cross-lingual language models (LM), such as mBERT, Unicoder and XLM, have achieved great success in cross-lingual representation learning. However, when applied to zero-shot cross-lingual transfer tasks, most existing methods use only single-language input for LM finetuning, without leveraging the intrinsic cross-lingual ali...More

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