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Getting Gender Right in Neural MT.

, , and . EMNLP, page 3003-3008. Association for Computational Linguistics, (2018)

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On the Integration of LinguisticFeatures into Statistical and Neural Machine Translation.. CoRR, (2020)Prediction of Emotions from Text using Sentiment Analysis for Expressive Speech Synthesis., , and . SSW, page 21-26. ISCA, (2016)Getting Gender Right in Neural MT., , and . EMNLP, page 3003-3008. Association for Computational Linguistics, (2018)SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags., and . ACL (3), page 67-73. Association for Computational Linguistics, (2018)Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation., , and . MTSummit (1), page 222-232. European Association for Machine Translation, (2019)A Case Study of Natural Gender Phenomena in Translation. A Comparison of Google Translate, Bing Microsoft Translator and DeepL for English to Italian, French and Spanish., , , and . CLiC-it, volume 2769 of CEUR Workshop Proceedings, CEUR-WS.org, (2020)ABI Neural Ensemble Model for Gender Prediction., , , , and . GxG@CLIN, volume 2453 of CEUR Workshop Proceedings, page 53-61. CEUR-WS.org, (2019)NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives., , and . EMNLP (1), page 8940-8948. Association for Computational Linguistics, (2021)"Vaderland", "Volk" and "Natie": Semantic Change Related to Nationalism in Dutch Literature Between 1700 and 1880 Captured with Dynamic Bernoulli Word Embeddings., , and . LChange@ACL, page 125-130. Association for Computational Linguistics, (2022)Europarl Datasets with Demographic Speaker Information., and . EAMT, (2018)