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The effect of domain and diacritics in Yorùbá-English neural machine translation
In: 18th Biennial Machine Translation Summit ; https://hal.inria.fr/hal-03350967 ; 18th Biennial Machine Translation Summit, Aug 2021, Orlando, United States (2021)
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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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A Data Augmentation Approach for Sign-Language-To-Text Translation In-The-Wild ...
Nunnari, Fabrizio; España-Bonet, Cristina; Avramidis, Eleftherios. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021
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The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation ...
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Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages ...
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8
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
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9
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.676/ Abstract: Traditional hand-crafted linguistically-informed features have often been used for distinguishing between translated and original non-translated texts. By contrast, to date, neural architectures without manual feature engineering have been less explored for this task. In this work, we (i) compare the traditional feature-engineering-based approach to the feature-learning-based one and (ii) analyse the neural architectures in order to investigate how well the hand-crafted features explain the variance in the neural models' predictions. We use pre-trained neural word embeddings, as well as several end-to-end neural architectures in both monolingual and multilingual settings and compare them to feature-engineering-based SVM classifiers. We show that (i) neural architectures outperform other approaches by more than 20 accuracy points, with the BERT-based model performing the best in both the monolingual and multilingual settings; (ii) ...
Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing
URL: https://underline.io/lecture/38089-comparing-feature-engineering-and-feature-learning-approaches-for-multilingual-translationese-classification
https://dx.doi.org/10.48448/fj59-8z46
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10
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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11
Tailoring and Evaluating the Wikipedia for in-Domain Comparable Corpora Extraction ...
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WTC1.1 (WikiTailor corpus v. 1.1) ...
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MT models for multilingual CLuBS engine (en-de-fr-es) ...
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14
WTC1.0 (WikiTailor corpus v. 1.0) ...
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15
WTC1.1 (WikiTailor corpus v. 1.1) ...
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16
MT models for multilingual CLuBS engine (en-de-fr-es) ...
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17
Multilingual and Interlingual Semantic Representations for Natural Language Processing: A Brief Introduction
In: Computational Linguistics, Vol 46, Iss 2, Pp 249-255 (2020) (2020)
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18
GeBioToolkit: Automatic Extraction of Gender-Balanced Multilingual Corpus of Wikipedia Biographies ...
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19
Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi ...
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20
Query Translation for Cross-lingual Search in the Academic Search Engine PubPsych ...
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