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AUTOLEX: An Automatic Framework for Linguistic Exploration ...
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SD-QA: Spoken Dialectal Question Answering for the Real World ...
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Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties ...
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Machine Translation into Low-resource Language Varieties ...
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Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors ...
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Systematic Inequalities in Language Technology Performance across the World's Languages ...
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Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling ...
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Investigating Post-pretraining Representation Alignment for Cross-Lingual Question Answering ...
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Towards More Equitable Question Answering Systems: How Much More Data Do You Need? ...
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Cross-Lingual Text Classification of Transliterated Hindi and Malayalam ...
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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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When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selection ...
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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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Towards Minimal Supervision BERT-based Grammar Error Correction ...
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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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It's not a Non-Issue: Negation as a Source of Error in Machine Translation ...
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Automatic Extraction of Rules Governing Morphological Agreement ...
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A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization ...
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Universal Phone Recognition with a Multilingual Allophone System ...
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