A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages (N19-1)
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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. |
| Approach: | They propose an approach for low-resource unsupervised part of speech (POS) tagging that yields fully grounded output and requires no labeled training data. |
| Outcome: | The proposed method achieves reasonable performance across languages, including Sinhalese and Kinyarwanda, with no labeled training data. |
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| Challenge: | Low-resource languages lack annotated data even for basic syntactic information such as parts of speech. |
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| Challenge: | Part-of-Speech (POS) tagging is an important component of the NLP pipeline, but many low-resource languages lack labeled training data. |
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Antonios Anastasopoulos, Marika Lekakou, Josep Quer, Eleni Zimianiti, Justin DeBenedetto, David Chiang
| Challenge: | a recent study examines POS tagging techniques on endangered languages . most natural language processing applications have been tested on only a handful of languages - a problem that is compounded by the lack of standard orthography. |
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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 . |
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| Challenge: | Part-of-Speech (POS) tags are routinely included in many NLP tasks. |
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| Challenge: | Using a crowdsourcing platform, we collected 18,917 annotations for a less-resourced French regional language, Alsatian. |
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| Challenge: | Large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks, but they fail to achieve state-of-the-art (SOTA) performance. |
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