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1
Can Synthetic Translations Improve Bitext Quality? ...
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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 ...
Abstract: This presentation describes the UMD submission to the Explainable Quality Estimation Shared Task at the Eval4NLP 2021 Workshop on ``Evaluation & Comparison of NLP Systems''. We participated in the word-level and sentence-level MT Quality Estimation (QE) constrained tasks for all language pairs: Estonian-English, Romanian-English, German-Chinese, and Russian-German. Our approach combines the predictions of a word-level explainer model on top of a sentence-level QE model and a sequence labeler trained on synthetic data. These models are based on pre-trained multilingual language models and do not require any word-level annotations for training, making them well suited to zero-shot settings. Our best performing system improves over the best baseline across all metrics and language pairs, with an average gain of 0.1 in AUC, Average Precision, and Recall at Top-K score. ...
Keyword: Machine Learning; Material Culture; Natural Language Processing
URL: https://underline.io/lecture/39301-the-umd-submission-to-the-explainable-mt-quality-estimation-shared-task-combining-explanation-models-with-sequence-labeling
https://dx.doi.org/10.48448/hxe1-f287
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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 ...
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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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