| Challenge: | Evaluating translation models is a trade-off between effort and detail. |
| Approach: | They propose to use a neural text classifier to automatically expose systematic differences between human and machine translations to human experts. |
| Outcome: | The proposed method exposes systematic differences between human and machine translations to human experts. |
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Shivendra Bhardwaj, David Alfonso Hermelo, Phillippe Langlais, Gabriel Bernier-Colborne, Cyril Goutte, Michel Simard
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Towards Modeling the Style of Translators in Neural Machine Translation (2021.naacl-main)
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| Challenge: | a key ingredient of neural machine translation is the use of large datasets with different but consistent translation styles . however, the models do not capture the variety of translators' styles from the data . a recent study shows that style-augmented models can capture the style variations of translator . |
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Lost in Translation, and Found: Detecting and Interpreting Translation Effects (2026.acl-long)
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Shira Wein, Anna Serbina, Jiyuan Ji, Nathan Wolf, Jason DeGraaff, Prajakta Kini, Maria Leonor Pacheco
| Challenge: | Translationese refers to the statistical patterns that distinguish translated texts from original texts. |
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| Challenge: | Current machine translation techniques are bottlenecked by adequacy issues . we propose automatic detection of missing and wrong translations . |
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Why Generate When You Can Discriminate? A Novel Technique for Text Classification using Language Models (2024.findings-eacl)
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A Discriminative Neural Model for Cross-Lingual Word Alignment (D19-1)
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| Challenge: | a novel word alignment model for machine translation has been developed for a number of languages . explicit word-to-word alignments have largely been lost in neural MT systems . |
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Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort (2021.acl-long)
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Discriminative Reranking for Neural Machine Translation (2021.acl-long)
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| Challenge: | reranking models allow the integration of rich features to select a better output hypothesis within an n-best list or lattice. |
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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. |
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