What data should I include in my POS tagging training set? (2025.findings-emnlp)
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| Challenge: | POS tagging is a crucial task for descriptive linguistics and language documentation . POS tags are not available in all languages, but are used for training sets for understudied languages . |
| Approach: | They compare POS tagging with in-context learning, active learning, and random sampling . they find that POS can deliver reasonable results for communities with limited resources . |
| Outcome: | The proposed training set for Indigenous and endangered languages performs better than random sampling. |
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| Challenge: | Part-of-Speech (POS) tags are routinely included in many NLP tasks. |
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| Challenge: | Existing studies on cross-lingual generalisability of large pre-trained models use English training data and test data in unseen languages. |
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Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text (L18-1)
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Géraldine Damnati, Jeremy Auguste, Alexis Nasr, Delphine Charlet, Johannes Heinecke, Frédéric Béchet
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| Challenge: | Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but taggers need to ground their clusters as well. |
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| Challenge: | a recent study shows that multi-task learning improves performance of NLP tasks by exploiting similarities between tasks. |
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Parsing linearizations appreciate PoS tags - but some are fussy about errors (2022.aacl-short)
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| Challenge: | Recent work on the impact of PoS tags on graph- and transition-based parsers suggests that they are only useful when tagging accuracy is prohibitively high or in low-resource scenarios. |
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Diversifying language models for lesser-studied languages and language-usage contexts: A case of second language Korean (2023.findings-emnlp)
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| Challenge: | Existing morpheme parsers/taggers do not work reliably and optimally for L2 data. |
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