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- W4385570079 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 substantial improvements over current state-of-the-art methods." @default.
- W4385570079 created "2023-08-05" @default.
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- W4385570079 date "2023-01-01" @default.
- W4385570079 modified "2023-10-18" @default.
- W4385570079 title "Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning" @default.
- W4385570079 doi "https://doi.org/10.18653/v1/2023.acl-long.222" @default.
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