Papers by Sourabh Deoghare
Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages (2024.findings-emnlp)
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| Challenge: | Existing studies on multilingual automatic post-editing systems for low-resource Indo-Aryan languages have focused on different models for different language pairs. |
| Approach: | They propose to use a multilingual automatic post-editing system to improve machine translations for low-resource Indo-Aryan languages. |
| Outcome: | The proposed model outperforms English-Hindi and English-Marathi models by 2.5 and 2.39 TER points. |
Quality Estimation-Assisted Automatic Post-Editing (2023.findings-emnlp)
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| Challenge: | Existing APE and QE combination strategies have not shown significant performance gains in the field of automatic post-editing (APE). |
| Approach: | They propose to train a model on APE and QE tasks to improve the APE performance by using a multi-task learning methodology that treats both tasks as a 'bargaining game' they also investigate various existing combination strategies and show that their approach achieves state-of-the-art performance for a ‘distant’ language pair, viz., English-Marathi. |
| Outcome: | The proposed model improves on two different language pairs, viz., English-Marathi and English-German. |
A Multi-task Learning Framework for Quality Estimation (2023.findings-acl)
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Sourabh Deoghare, Paramveer Choudhary, Diptesh Kanojia, Tharindu Ranasinghe, Pushpak Bhattacharyya, Constantin Orăsan
| Challenge: | Conventional approaches to QE involve training separate models at different levels of granularity viz., word-level, sentence-level and document-level . |
| Approach: | They propose to train a single model for sentence-level and word-level QE tasks in a multi-task learning framework and compare them to baseline models. |
| Outcome: | The proposed model improves on the single-pair, multi-patch, and zero-shot settings. |
Giving the Old a Fresh Spin: Quality Estimation-Assisted Constrained Decoding for Automatic Post-Editing (2025.naacl-short)
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| Challenge: | Existing methods to improve automatic post-editing (APE) systems struggle with over-correction, despite the principle of minimal editing. |
| Approach: | They propose a method that incorporates word-level Quality Estimation (QE) information during the decoding process. |
| Outcome: | The proposed method improves on English-German, English-Hindi, and English-Marathi language pairs, with TER gains of 0.65, 1.86, and 1.44 points, respectively. |