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Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer ...
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Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation ...
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The UMD Submission to the Explainable MT Quality Estimation Shared Task: Combining Explanation Models with Sequence Labeling ...
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Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer ...
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How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation? ...
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EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints ...
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A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification ...
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Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank ...
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Incorporating Terminology Constraints in Automatic Post-Editing ...
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EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints ...
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Controlling Neural Machine Translation Formality with Synthetic Supervision ...
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Controlling Text Complexity in Neural Machine Translation ...
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Formality Style Transfer Within and Across Languages with Limited Supervision
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Identifying Semantic Divergences in Parallel Text without Annotations ...
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Bi-Directional Neural Machine Translation with Synthetic Parallel Data ...
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Abstract:
Despite impressive progress in high-resource settings, Neural Machine Translation (NMT) still struggles in low-resource and out-of-domain scenarios, often failing to match the quality of phrase-based translation. We propose a novel technique that combines back-translation and multilingual NMT to improve performance in these difficult cases. Our technique trains a single model for both directions of a language pair, allowing us to back-translate source or target monolingual data without requiring an auxiliary model. We then continue training on the augmented parallel data, enabling a cycle of improvement for a single model that can incorporate any source, target, or parallel data to improve both translation directions. As a byproduct, these models can reduce training and deployment costs significantly compared to uni-directional models. Extensive experiments show that our technique outperforms standard back-translation in low-resource scenarios, improves quality on cross-domain tasks, and effectively reduces ... : Accepted at the 2nd Workshop on Neural Machine Translation and Generation (WNMT 2018) ...
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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.1805.11213 https://arxiv.org/abs/1805.11213
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Multi-Task Neural Models for Translating Between Styles Within and Across Languages ...
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