利用对比学习与原型学习改进跨语言命名实体识别的自训练
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning
AI总结：
提出ContProto框架，以对比自训练对齐跨语言表示并分离类别簇，同时用基于原型的伪标签降噪，显著提升跨语言NER性能。
摘要：
在跨语言命名实体识别（NER）中，自训练通常通过在伪标注的目标语言数据上进行训练来弥合语言差距。然而，由于在目标语言上的性能欠佳，伪标签往往含有噪声，并限制了整体性能。在本工作中，我们致力于通过在一个连贯框架中结合表示学习与伪标签精炼，改进面向跨语言NER的自训练。我们提出的方法名为ContProto，主要包含两个组成部分：（1）对比自训练；（2）基于原型的伪标签标注。我们的对比自训练通过分离不同类别的簇来促进跨度分类，并通过在源语言与目标语言之间生成紧密对齐的表示来增强跨语言可迁移性。同时，基于原型的伪标签标注有效提升了训练过程中伪标签的准确性。我们在多个迁移语言对上评估了ContProto，实验结果表明，相较于当前最先进方法，我们的方法带来了显著提升。
英文摘要：
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.
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