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1
Can Synthetic Translations Improve Bitext Quality? ...
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2
Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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3
Rule-based Morphological Inflection Improves Neural Terminology Translation ...
Xu, Weijia; Carpuat, Marine. - : arXiv, 2021
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4
Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer ...
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5
Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation ...
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6
The UMD Submission to the Explainable MT Quality Estimation Shared Task: Combining Explanation Models with Sequence Labeling ...
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7
Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer ...
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8
How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation? ...
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9
EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints ...
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10
A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification ...
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11
Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank ...
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12
Incorporating Terminology Constraints in Automatic Post-Editing ...
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13
EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints ...
Xu, Weijia; Carpuat, Marine. - : arXiv, 2020
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14
Controlling Neural Machine Translation Formality with Synthetic Supervision ...
Niu, Xing; Carpuat, Marine. - : arXiv, 2019
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15
Controlling Text Complexity in Neural Machine Translation ...
Agrawal, Sweta; Carpuat, Marine. - : arXiv, 2019
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16
Identifying Semantic Divergences Across Languages
Vyas, Yogarshi. - 2019
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17
Formality Style Transfer Within and Across Languages with Limited Supervision
Niu, Xing. - 2019
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18
Identifying Semantic Divergences in Parallel Text without Annotations ...
Abstract: Recognizing that even correct translations are not always semantically equivalent, we automatically detect meaning divergences in parallel sentence pairs with a deep neural model of bilingual semantic similarity which can be trained for any parallel corpus without any manual annotation. We show that our semantic model detects divergences more accurately than models based on surface features derived from word alignments, and that these divergences matter for neural machine translation. ... : Accepted as a full paper to NAACL 2018 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1803.11112
https://dx.doi.org/10.48550/arxiv.1803.11112
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19
Bi-Directional Neural Machine Translation with Synthetic Parallel Data ...
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20
Multi-Task Neural Models for Translating Between Styles Within and Across Languages ...
Niu, Xing; Rao, Sudha; Carpuat, Marine. - : arXiv, 2018
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