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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 BASE Show 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 Abstract: Cross-lingual resources such as parallel corpora and bilingual dictionaries are cornerstones of multilingual natural language processing (NLP). They have been used to study the nature of translation, train automatic machine translation systems, as well as to transfer models across languages for an array of NLP tasks. However, the majority of work in cross-lingual and multilingual NLP assumes that translations recorded in these resources are semantically equivalent. This is often not the case---words and sentences that are considered to be translations of each other frequently divergein meaning, often in systematic ways. In this thesis, we focus on such mismatches in meaning in text that we expect to be aligned across languages. We term such mismatches as cross-lingual semantic divergences. The core claim of this thesis is that translation is not always meaning preserving which leads to cross-lingual semantic divergences that affect multilingual NLP tasks. Detecting such divergences requires ways of directly characterizing differences in meaning across languages through novel cross-lingual tasks, as well as models that account for translation ambiguity and do not rely on expensive, task-specific supervision. We support this claim through three main contributions. First, we show that a large fraction of data in multilingual resources (such as parallel corpora and bilingual dictionaries) is identified as semantically divergent by human annotators. Second, we introduce cross-lingual tasks that characterize differences in word meaning across languages by identifying the semantic relation between two words. We also develop methods to predict such semantic relations, as well as a model to predict whether sentences in different languages have the same meaning. Finally, we demonstrate the impact of divergences by applying the methods developed in the previous sections to two downstream tasks. We first show that our model for identifying semantic relations between words helps in separating equivalent word translations from divergent translations in the context of bilingual dictionary induction, even when the two words are close in meaning. We also show that identifying and filtering semantic divergences in parallel data helps in training a neural machine translation system twice as fast without sacrificing quality. Keyword: Computer science; lexical semantics; Linguistics; machine learning; machine translation; multilingual nlp; natural language processing URL: http://hdl.handle.net/1903/25448 https://doi.org/10.13016/rymp-ymgo BASE Hide 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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