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MasakhaNER: Named entity recognition for African languages
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In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021)
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Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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Explorations in Transfer Learning for OCR Post-Correction ...
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Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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Lexically Aware Semi-Supervised Learning for OCR Post-Correction ...
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Lexically-Aware Semi-Supervised Learning for OCR Post-Correction ...
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Dependency Induction Through the Lens of Visual Perception ...
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Dependency Induction Through the Lens of Visual Perception ...
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AlloVera: a multilingual allophone database
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In: LREC 2020: 12th Language Resources and Evaluation Conference ; https://halshs.archives-ouvertes.fr/halshs-02527046 ; LREC 2020: 12th Language Resources and Evaluation Conference, European Language Resources Association, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/ (2020)
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AlloVera: A Multilingual Allophone Database ...
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Mortensen, David R.; Li, Xinjian; Littell, Patrick; Michaud, Alexis; Rijhwani, Shruti; Anastasopoulos, Antonios; Black, Alan W.; Metze, Florian; Neubig, Graham. - : arXiv, 2020
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Abstract:
We introduce a new resource, AlloVera, which provides mappings from 218 allophones to phonemes for 14 languages. Phonemes are contrastive phonological units, and allophones are their various concrete realizations, which are predictable from phonological context. While phonemic representations are language specific, phonetic representations (stated in terms of (allo)phones) are much closer to a universal (language-independent) transcription. AlloVera allows the training of speech recognition models that output phonetic transcriptions in the International Phonetic Alphabet (IPA), regardless of the input language. We show that a "universal" allophone model, Allosaurus, built with AlloVera, outperforms "universal" phonemic models and language-specific models on a speech-transcription task. We explore the implications of this technology (and related technologies) for the documentation of endangered and minority languages. We further explore other applications for which AlloVera will be suitable as it grows, ... : 8 pages, LREC 2020 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2004.08031 https://arxiv.org/abs/2004.08031
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A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization ...
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Temporally-Informed Analysis of Named Entity Recognition ...
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Temporally-Informed Analysis of Named Entity Recognition ...
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AlloVera: a multilingual allophone database
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In: LREC 2020: 12th Language Resources and Evaluation Conference ; https://halshs.archives-ouvertes.fr/halshs-02527046 ; LREC 2020: 12th Language Resources and Evaluation Conference, European Language Resources Association, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/ (2020)
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Improving Candidate Generation for Low-resource Cross-lingual Entity Linking
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In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 109-124 (2020) (2020)
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Zero-shot Neural Transfer for Cross-lingual Entity Linking ...
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