WAD-X: Improving Zero-shot Cross-lingual Transfer via Adapter-based Word Alignment
Ahtamjan Ahmat,
Yating Yang,
Bo Ma
et al.
Abstract:Multilingual pre-trained language models (mPLMs) have achieved remarkable performance on zero-shot cross-lingual transfer learning. However, most mPLMs implicitly encourage cross-lingual alignment in pre-training stage, making it hard to capture accurate word alignment across languages. In this paper, we propose Word-align ADapters for Cross-lingual transfer (WAD-X) to explicitly align word representations of mPLMs via language-specific subspace. Taking a mPLM as the backbone model, WAD-X constructs subspace f… Show more
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