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Hits 1 – 6 of 6
1
Europarl Direct Translationese Dataset ...
Amponsah-Kaakyire, Kwabena
;
Pylypenko, Daria
;
España-Bonet, Cristina
. - : Zenodo, 2021
BASE
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2
Europarl Direct Translationese Dataset ...
Amponsah-Kaakyire, Kwabena
;
Pylypenko, Daria
;
España-Bonet, Cristina
. - : Zenodo, 2021
BASE
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3
Europarl Direct Translationese Dataset ...
Amponsah-Kaakyire, Kwabena
;
Pylypenko, Daria
;
España-Bonet, Cristina
. - : Zenodo, 2021
BASE
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4
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
Pylypenko, Daria
;
Amponsah-Kaakyire, Kwabena
;
Chowdhury, Koel Dutta
. - : arXiv, 2021
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5
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Amponsah-Kaakyire, Kwabena
;
Dutta Chowdhury, Koel
;
España-Bonet, Cristina
;
Pylypenko, Daria
;
van Genabith, Josef
. - : Underline Science Inc., 2021
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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6
Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi ...
Alabi, Jesujoba O.
;
Amponsah-Kaakyire, Kwabena
;
Adelani, David I.
. - : arXiv, 2019
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