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Few-shot learning through contextual data augmentation
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In: EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03121971 ; EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2021, Kiev / Virtual, Ukraine (2021)
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Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking ...
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CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification ...
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Exploring Unsupervised Pretraining Objectives for Machine Translation ...
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Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking ...
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Architecture of a Scalable, Secure and Resilient Translation Platform for Multilingual News Media
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In: Proceedings of the 1st International Workshop on Language Technology Platforms ; 1st International Workshop on Language Technology Platforms ; https://hal.archives-ouvertes.fr/hal-02900633 ; 1st International Workshop on Language Technology Platforms, 2020, Marseille, France. pp.16-21 (2020)
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The University of Edinburgh’s English-Tamil and English-Inuktitut Submissions to the WMT20 News Translation Task
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In: Proceedings of the 5th Conference on Machine Translation ; 5th Conference on Machine Translation ; https://hal.archives-ouvertes.fr/hal-02981153 ; 5th Conference on Machine Translation, Nov 2020, Online, Unknown Region (2020)
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Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations ...
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Architecture of a Scalable, Secure and Resilient Translation Platform for Multilingual News Media ...
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Architecture of a Scalable, Secure and Resilient Translation Platform for Multilingual News Media ...
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A Latent Morphology Model for Open-Vocabulary Neural Machine Translation ...
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Abstract:
Translation into morphologically-rich languages challenges neural machine translation (NMT) models with extremely sparse vocabularies where atomic treatment of surface forms is unrealistic. This problem is typically addressed by either pre-processing words into subword units or performing translation directly at the level of characters. The former is based on word segmentation algorithms optimized using corpus-level statistics with no regard to the translation task. The latter learns directly from translation data but requires rather deep architectures. In this paper, we propose to translate words by modeling word formation through a hierarchical latent variable model which mimics the process of morphological inflection. Our model generates words one character at a time by composing two latent representations: a continuous one, aimed at capturing the lexical semantics, and a set of (approximately) discrete features, aimed at capturing the morphosyntactic function, which are shared among different surface ...
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URL: https://dx.doi.org/10.5281/zenodo.3923498 https://zenodo.org/record/3923498
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A Latent Morphology Model for Open-Vocabulary Neural Machine Translation ...
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The University of Edinburgh’s Submissions to the WMT19 News Translation Task
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In: 4th Conference on Machine Translation ; https://hal.inria.fr/hal-02986330 ; 4th Conference on Machine Translation, 2019, Florence, Italy (2019)
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Towards a Multi-view Language Representation: A Shared Space of Discrete and Continuous Language Features ...
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Towards a Multi-view Language Representation: A Shared Space of Discrete and Continuous Language Features ...
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On the Importance of Word Boundaries in Character-level Neural Machine Translation ...
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A Latent Morphology Model for Open-Vocabulary Neural Machine Translation ...
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Generalization in Generation: A closer look at Exposure Bias
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In: Proceedings of the 3rd Workshop on Neural Generation and Translation (2019)
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