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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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A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization ...
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The Indigenous Languages Technology Project at NRC Canada: an empowerment-oriented approach to developing language software ...
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Universal Phone Recognition with a Multilingual Allophone System ...
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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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Learning Language Representations for Typology Prediction ...
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STREAMLInED Challenges: Aligning Research Interests with Shared Tasks
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In: 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages, March 6-7, 2017. Honolulu, Hawai‘i, USA (2017)
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Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning ...
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Tsvetkov, Yulia; Sitaram, Sunayana; Faruqui, Manaal; Lample, Guillaume; Littell, Patrick; Mortensen, David; Black, Alan W; Levin, Lori; Dyer, Chris. - : arXiv, 2016
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Abstract:
We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on typological information about the language to be predicted. We apply these to the problem of modeling phone sequences---a domain in which universal symbol inventories and cross-linguistically shared feature representations are a natural fit. Intrinsic evaluation on held-out perplexity, qualitative analysis of the learned representations, and extrinsic evaluation in two downstream applications that make use of phonetic features show (i) that polyglot models better generalize to held-out data than comparable monolingual models and (ii) that polyglot phonetic feature representations are of higher quality than those learned monolingually. ... : Proceedings of NAACL 2016; 10 pages ...
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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.1605.03832 https://arxiv.org/abs/1605.03832
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Kwak'wala "Agreement" as Partial Subject Copy
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In: LSA Annual Meeting Extended Abstracts; Vol 3: LSA Annual Meeting Extended Abstracts 2012; 11:1-5 ; 2377-3367 (2012)
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