In cross-lingual named entity recognition (NER), self-training is commonly
used to bridge the linguistic gap by training on pseudo-labeled target-language
data. However, due to sub-optimal performance on target languages, the pseudo
labels are often noisy and limit the overall performance. In this work, we aim
to improve self-training for cross-lingual NER by combining representation
learning and pseudo label refinement in one coherent framework. Our proposed
method, namely ContProto mainly comprises two components: (1) contrastive
self-training and (2) prototype-based pseudo-labeling. Our contrastive
self-training facilitates span classification by separating clusters of
different classes, and enhances cross-lingual transferability by producing
closely-aligned representations between the source and target language.
Meanwhile, prototype-based pseudo-labeling effectively improves the accuracy of
pseudo labels during training. We evaluate ContProto on multiple transfer
pairs, and experimental results show our method brings in substantial
improvements over current state-of-the-art methods.

本文提出了一种名为 ContProto 的跨语言命名实体识别的自我训练框架，大大提高了伪标签的准确性和不同语言间的转移能力，并在实验中展现了高于现有技术的表现。