Papers by Daria Pylypenko
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification (2021.emnlp-main)
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Daria Pylypenko, Kwabena Amponsah-Kaakyire, Koel Dutta Chowdhury, Josef van Genabith, Cristina España-Bonet
| Challenge: | Traditional hand-crafted features have been used for distinguishing between translated and original non-translated texts. |
| Approach: | They compare a feature-engineering-based approach to a features-learning-based one and use pre-trained neural word embeddings to train neural architectures. |
| Outcome: | The proposed approach outperforms other approaches by more than 20 accuracy points and the BERT-based model performs the best in both monolingual and multilingual settings. |