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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 24 1 Can Synthetic Translations Improve Bitext Quality? ... Briakou, Eleftheria; Carpuat, Marine. - : arXiv, 2022 BASE Show details 2 Rule-based Morphological Inflection Improves Neural Terminology Translation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Carpuat, Marine; Xu, Weijia. - : Underline Science Inc., 2021 BASE Show details 3 Rule-based Morphological Inflection Improves Neural Terminology Translation ... Xu, Weijia; Carpuat, Marine. - : arXiv, 2021 BASE Show details 4 Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer ... Briakou, Eleftheria; Agrawal, Sweta; Tetreault, Joel. - : arXiv, 2021 BASE Show details 5 Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Briakou, Eleftheria; Carpuat, Marine. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.562 Abstract: While it has been shown that Neural Machine Translation (NMT) is highly sensitive to noisy parallel training samples, prior work treats all types of mismatches between source and target as noise. As a result, it remains unclear how samples that are mostly equivalent but contain a small number of semantically divergent tokens impact NMT training. To close this gap, we analyze the impact of different types of fine-grained semantic divergences on Transformer models. We show that models trained on synthetic divergences output degenerated text more frequently and are less confident in their predictions. Based on these findings, we introduce a divergent-aware NMT framework that uses factors to help NMT recover from the degradation caused by naturally occurring divergences, improving both translation quality and model calibration on EN-FR tasks. ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/25802-beyond-noise-mitigating-the-impact-of-fine-grained-semantic-divergences-on-neural-machine-translation https://dx.doi.org/10.48448/ac9b-d890 BASE Hide details 6 The UMD Submission to the Explainable MT Quality Estimation Shared Task: Combining Explanation Models with Sequence Labeling ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Carpuat, Marine; Kabir, Tasnim. - : Underline Science Inc., 2021 BASE Show details 7 Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Joel; Agrawal, Sweta. - : Underline Science Inc., 2021 BASE Show details 8 How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation? ... Xu, Weijia; Ma, Shuming; Zhang, Dongdong. - : arXiv, 2021 BASE Show details 9 EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Carpuat, Marine; Xu, Weijia. - : Underline Science Inc., 2021 BASE Show details 10 A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Agrawal, Sweta; Carpuat, Marine. - : Underline Science Inc., 2021 BASE Show details 11 Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank ... Briakou, Eleftheria; Carpuat, Marine. - : arXiv, 2020 BASE Show details 12 Incorporating Terminology Constraints in Automatic Post-Editing ... Wan, David; Kedzie, Chris; Ladhak, Faisal. - : arXiv, 2020 BASE Show details 13 EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints ... Xu, Weijia; Carpuat, Marine. - : arXiv, 2020 BASE Show details 14 Controlling Neural Machine Translation Formality with Synthetic Supervision ... Niu, Xing; Carpuat, Marine. - : arXiv, 2019 BASE Show details 15 Controlling Text Complexity in Neural Machine Translation ... Agrawal, Sweta; Carpuat, Marine. - : arXiv, 2019 BASE Show details 16 Identifying Semantic Divergences Across Languages Vyas, Yogarshi. - 2019 BASE Show details 17 Formality Style Transfer Within and Across Languages with Limited Supervision Niu, Xing. - 2019 BASE Show details 18 Identifying Semantic Divergences in Parallel Text without Annotations ... Vyas, Yogarshi; Niu, Xing; Carpuat, Marine. - : arXiv, 2018 BASE Show details 19 Bi-Directional Neural Machine Translation with Synthetic Parallel Data ... Niu, Xing; Denkowski, Michael; Carpuat, Marine. - : arXiv, 2018 BASE Show details 20 Multi-Task Neural Models for Translating Between Styles Within and Across Languages ... Niu, Xing; Rao, Sudha; Carpuat, Marine. - : arXiv, 2018 BASE Show details Page: 1 2

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