Papers by Koel Dutta Chowdhury
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. |
Towards Debiasing Translation Artifacts (2022.naacl-main)
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| Challenge: | Existing studies show translation artifacts in translations influence performance of cross-lingual tasks. |
| Approach: | They propose a method to reduce translation artifacts by extending an established bias-removal technique. |
| Outcome: | The proposed method reduces translationese at sentence and word level . it is the first study to debias translations on a natural language inference task . |
Understanding Translationese in Multi-view Embedding Spaces (2020.coling-main)
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| Challenge: | Recent studies show footprints of the source language remain visible in translations . this is the first time departures from isomorphism between embedding spaces are used to track translationese. |
| Approach: | They exploit departures from isomorphism between spaces built from original target language and translations into this target language to predict relations between languages in an unsupervised way. |
| Outcome: | The proposed method exploits departures from isomorphism between embedding spaces to predict relations between languages in an unsupervised way. |
Understanding the Effect of Textual Adversaries in Multimodal Machine Translation (D19-64)
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| Challenge: | Existing studies show that multimodal machine translation systems are better than text-only systems at translating phrases that have a direct correspondence in the image. |
| Approach: | They conduct experiments with both visual and textual adversaries to understand the role of textual inputs in multimodal machine translation. |
| Outcome: | The proposed model can recover masked tokens in the source sentences . the proposed model is based on a model with a visual modality . |
When Flores Bloomz Wrong: Cross-Direction Contamination in Machine Translation Evaluation (2026.eacl-short)
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| Challenge: | Large language models (LLMs) can be benchmark-contaminated, resulting in inflated scores that mask memorization as generalization. |
| Approach: | They use the FLORES-200 translation benchmark as a diagnostic to investigate cross-direction data contamination. |
| Outcome: | The proposed model can be cross-directional, boosting performance in unseen translation directions due to target-side memorization. |