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Europarl Direct Translationese Dataset ...
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Europarl Direct Translationese Dataset ...
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Europarl Direct Translationese Dataset ...
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Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages ...
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Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
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Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output ...
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Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation ...
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Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
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Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation ...
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A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment ...
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Automatic classification of human translation and machine translation : a study from the perspective of lexical diversity
Fu, Yingxue; Nederhof, Mark Jan. - : Linkoping University Electronic Press, 2021
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Transformer-based NMT : modeling, training and implementation
Xu, Hongfei. - : Saarländische Universitäts- und Landesbibliothek, 2021
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
In: Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02892154 ; Language Resources and Evaluation Conference, ELDA/ELRA, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/en/ (2020)
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
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Linguistically inspired morphological inflection with a sequence to sequence model ...
Abstract: Inflection is an essential part of every human language's morphology, yet little effort has been made to unify linguistic theory and computational methods in recent years. Methods of string manipulation are used to infer inflectional changes; our research question is whether a neural network would be capable of learning inflectional morphemes for inflection production in a similar way to a human in early stages of language acquisition. We are using an inflectional corpus (Metheniti and Neumann, 2020) and a single layer seq2seq model to test this hypothesis, in which the inflectional affixes are learned and predicted as a block and the word stem is modelled as a character sequence to account for infixation. Our character-morpheme-based model creates inflection by predicting the stem character-to-character and the inflectional affixes as character blocks. We conducted three experiments on creating an inflected form of a word given the lemma and a set of input and target features, comparing our architecture to ... : 13 pages, 6 figures ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2009.02073
https://dx.doi.org/10.48550/arxiv.2009.02073
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Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers ...
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Language service provision in the 21st century: challenges, opportunities and educational perspectives for translation studies
In: ISBN: 9788869234934 ; Bologna Process beyond 2020: Fundamental values of the EHEA pp. 297-303 (2020)
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Deep interactive text prediction and quality estimation in translation interfaces
Hokamp, Christopher M.. - : Dublin City University. School of Computing, 2018
In: Hokamp, Christopher M. (2018) Deep interactive text prediction and quality estimation in translation interfaces. PhD thesis, Dublin City University. (2018)
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