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Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages ...
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Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
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Linguistically inspired morphological inflection with a sequence to sequence model ...
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Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers ...
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INFODENS: An Open-source Framework for Learning Text Representations ...
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Massively Multilingual Neural Grapheme-to-Phoneme Conversion ...
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Abstract:
Grapheme-to-phoneme conversion (g2p) is necessary for text-to-speech and automatic speech recognition systems. Most g2p systems are monolingual: they require language-specific data or handcrafting of rules. Such systems are difficult to extend to low resource languages, for which data and handcrafted rules are not available. As an alternative, we present a neural sequence-to-sequence approach to g2p which is trained on spelling--pronunciation pairs in hundreds of languages. The system shares a single encoder and decoder across all languages, allowing it to utilize the intrinsic similarities between different writing systems. We show an 11% improvement in phoneme error rate over an approach based on adapting high-resource monolingual g2p models to low-resource languages. Our model is also much more compact relative to previous approaches. ... : EMNLP 2017 Workshop on Building Linguisically Generalizable NLP Systems ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1708.01464 https://dx.doi.org/10.48550/arxiv.1708.01464
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An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification ...
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Predicting the Law Area and Decisions of French Supreme Court Cases ...
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