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Viable Dependency Parsing as Sequence Labeling
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We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BiLSTM-based model it is possible to obtain fast and accurate parsers. These parsers are conceptually simple, not needing traditional parsing algorithms or auxiliary structures. However, experiments on the PTB and a sample of UD treebanks show that they provide a good speed-accuracy tradeoff, with results competitive with more complex approaches.
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Cited by 1 Pith paper
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Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages
A prompt-based GPT-4.1 pipeline with expert review produces UD-style dependency annotations for code-switched Spanish-English and Spanish-Guaraní text, including the first Spanish-Guaraní UD code-switched corpus.
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