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Learning the Ordering of Coordinate Compounds and Elaborate Expressions in Hmong, Lahu, and Chinese ...
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AUTOLEX: An Automatic Framework for Linguistic Exploration ...
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Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties ...
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Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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Tusom2021: A Phonetically Transcribed Speech Dataset from an Endangered Language for Universal Phone Recognition Experiments ...
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Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
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Differentiable Allophone Graphs for Language-Universal Speech 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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Towards Zero-shot Learning for Automatic Phonemic Transcription ...
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Abstract:
Automatic phonemic transcription tools are useful for low-resource language documentation. However, due to the lack of training sets, only a tiny fraction of languages have phonemic transcription tools. Fortunately, multilingual acoustic modeling provides a solution given limited audio training data. A more challenging problem is to build phonemic transcribers for languages with zero training data. The difficulty of this task is that phoneme inventories often differ between the training languages and the target language, making it infeasible to recognize unseen phonemes. In this work, we address this problem by adopting the idea of zero-shot learning. Our model is able to recognize unseen phonemes in the target language without any training data. In our model, we decompose phonemes into corresponding articulatory attributes such as vowel and consonant. Instead of predicting phonemes directly, we first predict distributions over articulatory attributes, and then compute phoneme distributions with a customized ... : AAAI 2020 ...
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Keyword:
Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
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URL: https://dx.doi.org/10.48550/arxiv.2002.11781 https://arxiv.org/abs/2002.11781
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Automatic Extraction of Rules Governing Morphological Agreement ...
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Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods ...
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Universal Phone Recognition with a Multilingual Allophone System ...
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Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks ...
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Characterizing Sociolinguistic Variation in the Competing Vaccination Communities ...
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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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Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation ...
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Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations ...
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