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• W4383428235 abstract "Syntactic parsing is a highly linguistic processing task whose parser requires training on treebanks from the expensive human annotation. As it is unlikely to obtain a treebank for every human language, in this work, we propose an effective cross-lingual UD parsing framework for transferring parser from only one source monolingual treebank to any other target languages without treebank available. To reach satisfactory parsing accuracy among quite different languages, we introduce two language modeling tasks into the training process of dependency parsing as multi-tasking. Assuming only unlabeled data from target languages plus the source treebank can be exploited together, we adopt a self-training strategy for further performance improvement in terms of our multi-task framework. Our proposed cross-lingual parsers are implemented for English, Chinese, and 29 UD treebanks. The empirical study shows that our cross-lingual parsers yield promising results for all target languages, approaching the parser performance which is trained in its own target treebank." @default.
• W4383428235 created "2023-07-07" @default.
• W4383428235 creator A5002584135 @default.
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• W4383428235 date "2023-11-01" @default.
• W4383428235 modified "2023-10-09" @default.
• W4383428235 title "Cross-Lingual Universal Dependency Parsing Only From One Monolingual Treebank" @default.
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• W4383428235 doi "https://doi.org/10.1109/tpami.2023.3291388" @default.
• W4383428235 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37410651" @default.
• W4383428235 hasPublicationYear "2023" @default.
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