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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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Abstract:
While the field of style transfer (ST) has been growing rapidly, it has been hampered by a lack of standardized practices for automatic evaluation. In this paper, we evaluate leading ST automatic metrics on the oft-researched task of formality style transfer. Unlike previous evaluations, which focus solely on English, we expand our focus to Brazilian-Portuguese, French, and Italian, making this work the first multilingual evaluation of metrics in ST. We outline best practices for automatic evaluation in (formality) style transfer and identify several models that correlate well with human judgments and are robust across languages. We hope that this work will help accelerate development in ST, where human evaluation is often challenging to collect. ... : EMNLP 2021 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2110.10668 https://dx.doi.org/10.48550/arxiv.2110.10668
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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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Multi-Task Neural Models for Translating Between Styles Within and Across Languages ...
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