  We propose a novel approach for cross-lingual Named Entity Recognition (NER)
zero-shot transfer using parallel corpora. We built an entity alignment model
on top of XLM-RoBERTa to project the entities detected on the English part of
the parallel data to the target language sentences, whose accuracy surpasses
all previous unsupervised models. With the alignment model we can get
pseudo-labeled NER data set in the target language to train task-specific
model. Unlike using translation methods, this approach benefits from natural
fluency and nuances in target-language original corpus. We also propose a
modified loss function similar to focal loss but assigns weights in the
opposite direction to further improve the model training on noisy
pseudo-labeled data set. We evaluated this proposed approach over 4 target
languages on benchmark data sets and got competitive F1 scores compared to most
recent SOTA models. We also gave extra discussions about the impact of parallel
corpus size and domain on the final transfer performance.
