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Machine Translation from Signed to Spoken Languages: State of the Art and Challenges ...
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NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives ...
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A Novel Pipeline for Domain Detection and Selecting In-domain Sentences in Machine Translation Systems ...
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A Novel Pipeline for Domain Detection and Selecting In-domain Sentences in Machine Translation Systems ...
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A Novel Pipeline for Domain Detection and Selecting In-domain Sentences in Machine Translation Systems ...
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NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives ...
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Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation ...
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Defining meaningful units. Challenges in sign segmentation and segment-meaning mapping ; 1st International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL 2021)
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Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation
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In: Soto, Xabier orcid:0000-0002-3622-6496 , Shterionov, Dimitar orcid:0000-0001-6300-797X , Poncelas, Alberto orcid:0000-0002-5089-1687 and Way, Andy orcid:0000-0001-5736-5930 (2020) Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation. In: Annual Conference of the Association for Computational Linguistics, ACL, 5-10 July 2020, Seattle, WA, USA (Online). (2020)
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Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation ...
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Towards language-agnostic alignment of product titles and descriptions: a neural approach
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In: Stein, Daniel, Shterionov, Dimitar orcid:0000-0001-6300-797X and Way, Andy orcid:0000-0001-5736-5930 (2019) Towards language-agnostic alignment of product titles and descriptions: a neural approach. In: 2019 World Wide Web Conference, 13-17 May 2019, San Francisco, USA. ISBN 978-1-4503-6675-5 (2019)
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APE through neural and statistical MT with augmented data: ADAPT/DCU submission to the WMT 2019 APE Shared task
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In: Shterionov, Dimitar orcid:0000-0001-6300-797X , Wagner, Joachim orcid:0000-0002-8290-3849 and do Carmo, Félix orcid:0000-0003-4193-3854 (2019) APE through neural and statistical MT with augmented data: ADAPT/DCU submission to the WMT 2019 APE Shared task. In: Fourth Conference on Machine Translation (WMT19), 01-02 Aug 2019, Florence, Italy. (2019)
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Lost in translation: loss and decay of linguistic richness in machine translation
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In: Way, Andy orcid:0000-0001-5736-5930 , Shterionov, Dimitar orcid:0000-0001-6300-797X and Vanmassenhove, Eva orcid:0000-0003-1162-820X (2019) Lost in translation: loss and decay of linguistic richness in machine translation. In: MT Summit XVII, 19-23 Aug 2019, Dublin,Ireland. (2019)
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Combining SMT and NMT back-translated data for efficient NMT
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In: Poncelas, Alberto orcid:0000-0002-5089-1687 , Popović, Maja orcid:0000-0001-8234-8745 , Shterionov, Dimitar orcid:0000-0001-6300-797X , Maillette de Buy Wenniger, Gideon and Way, Andy orcid:0000-0001-5736-5930 (2019) Combining SMT and NMT back-translated data for efficient NMT. In: Recent Advances in Natural Language Processing (RANLP 2019), 2-4 Sept 2019, Varna, Bulgaria. (2019)
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ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction ...
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Abstract:
We present our system for the CLIN29 shared task on cross-genre gender detection for Dutch. We experimented with a multitude of neural models (CNN, RNN, LSTM, etc.), more "traditional" models (SVM, RF, LogReg, etc.), different feature sets as well as data pre-processing. The final results suggested that using tokenized, non-lowercased data works best for most of the neural models, while a combination of word clusters, character trigrams and word lists showed to be most beneficial for the majority of the more "traditional" (that is, non-neural) models, beating features used in previous tasks such as n-grams, character n-grams, part-of-speech tags and combinations thereof. In contradiction with the results described in previous comparable shared tasks, our neural models performed better than our best traditional approaches with our best feature set-up. Our final model consisted of a weighted ensemble model combining the top 25 models. Our final model won both the in-domain gender prediction task and the ... : Conference: Computational Linguistics of the Netherlands CLIN29 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1902.08856 https://dx.doi.org/10.48550/arxiv.1902.08856
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Integration of a Multilingual Preordering Component into a Commercial SMT Platform
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 61-72 (2017) (2017)
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