Papers by Constantin Orăsan
PLOD: An Abbreviation Detection Dataset for Scientific Documents (2022.lrec-1)
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| Challenge: | Existing datasets for abbreviation detection and extraction are limited. |
| Approach: | They propose to use a large-scale dataset for abbreviation detection and extraction that contains 160k+ segments automatically annotated with abbrevian and long forms. |
| Outcome: | The proposed dataset has an F1 score of 0.92 for abbreviations and 0.89 for detecting their corresponding long forms. |
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. |