Enhancing Zero-Shot Cross-Lingual NER via Synthetic Unlabeled Data and Projection-Based Transfer Beyond Multilingual Pre-training

Abstract

Abstract: Multilingual BERT (mBERT), a language model pre-trained on large multilingual corpora, has impressive zero-shot cross-lingual transfer capabilities and performs surprisingly well on zero-shot POS tagging and Named Entity Recognition (NER), as well as on cross-lingual model transfer. At present, the mainstream methods to solve the cross-lingual downstream tasks are always using the last transformer layer's output of mBERT as the representation of linguistic information. In this work, we explore the complementary property of lower layers to the last transformer layer of mBERT. A feature aggregat

Research Question

Does integrating synthetic unlabeled data with projection-based transfer enhance zero-shot cross-lingual NER performance more effectively than multilingual pre-training alone?

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