Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning

• URL: http://arxiv.org/abs/2305.13628v2
• Date: Sun, 4 Jun 2023 16:32:41 GMT
• Title: Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning
• Authors: Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Chunyan Miao
• Abstract summary: In cross-lingual named entity recognition, self-training is commonly used to bridge the linguistic gap. In this work, we aim to improve self-training for cross-lingual NER by combining representation learning and pseudo label refinement. Our proposed method, namely ContProto mainly comprises two components: (1) contrastive self-training and (2) prototype-based pseudo-labeling.
• Score: 80.08139343603956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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