Frustratingly Easy Label Projection for Cross-lingual Transfer (2023.findings-acl)
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| Challenge: | Existing approaches to improve cross-lingual transfer performance are based on word alignment, but no empirical studies have evaluated their effectiveness or limitations. |
| Approach: | They propose a mark-then-translate method that integrates translation and projection by inserting special markers around the labeled spans in the original sentence. |
| Outcome: | The proposed method outperforms word alignment-based methods in 57 languages and three tasks. |
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| Challenge: | Label projection is an effective technique for cross-lingual transfer, extending span-annotated datasets from high-resource languages to low-resourced ones. |
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| Challenge: | Annotation projection is a task of transporting labels from source to target language without high-quality training data. |
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The Devil Is in the Word Alignment Details: On Translation-Based Cross-Lingual Transfer for Token Classification Tasks (2025.findings-acl)
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| Challenge: | Translation-based strategies for cross-lingual transfer XLT include label projection . word aligners (WAs) are commonly used for label projection, but low-level design decisions for using them have not been investigated . |
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| Challenge: | Existing methods for label projection are inaccurate or slow for large-scale use. |
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| Challenge: | Prior work favors simplified label translation or relying on word-level alignments for label projection. |
| Approach: | They propose a novel approach CLaP which translates text to target language and performs *contextual translation* on the labels using the translated text as the context. |
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Automatic Bilingual Markup Transfer (2021.findings-emnlp)
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| Challenge: | Existing work on markup transfer is performed with machine translation . a human translator generates the target translation without markup, and then the system infers the placement of markup tags. |
| Approach: | They propose two metrics and evaluate several approaches to bilingual markup transfer . best approach achieves an average accuracy of 94.7% across six language pairs . |
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Constraining word alignments with posterior regularization for label transfer (2022.naacl-industry)
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| Challenge: | Unsupervised word alignments are not always possible in industrial NLP pipelines, where multilingual annotation guidelines are complex and deviate from semantic consistency due to various factors. |
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Enhanced Language Representation with Label Knowledge for Span Extraction (2021.emnlp-main)
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| Challenge: | Existing approaches to extract text spans from plain text do not fully exploit label knowledge. |
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Cross-lingual Annotation Projection in Legal Texts (2020.coling-main)
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| Challenge: | a new study examines annotation projection in text classification problems where source documents are published in multiple languages. |
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Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections (P19-1)
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| Challenge: | Current systems for syntactic analysis tasks rely heavily on large scale annotated data. |
| Approach: | They propose to learn a generative model with a structured prior that uses labeled source and unlabeled target data jointly. |
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