Papers by Onur Çelebi
Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages (2022.findings-emnlp)
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| Challenge: | a new study aims to extend multilingual representation learning beyond the hundred most frequent languages . current work on multilingual sentence representations has focused on training one model which handles all languages of interest . |
| Approach: | They propose a teacher-student approach to extend existing monolingual sentence embedding space to new languages. |
| Outcome: | The proposed model outperforms the original LASER encoder in 44 African languages . the model can be used to train multiple languages and learn new languages if they have the same training data . |
xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languages (2023.acl-short)
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| Challenge: | xsim++ provides a reliable proxy for bitext mining without expensive pipelines. |
| Approach: | They propose a new proxy proxy based on similarity in a multilingual embedding space . they validate this proxy by running a significant number of bitext mining experiments for a set of low-resource languages and then train NMT systems on the mined data. |
| Outcome: | The proposed proxy improves on xsim++ and trains on the mined data. |
stopes - Modular Machine Translation Pipelines (2022.emnlp-demos)
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Pierre Andrews, Guillaume Wenzek, Kevin Heffernan, Onur Çelebi, Anna Sun, Ammar Kamran, Yingzhe Guo, Alexandre Mourachko, Holger Schwenk, Angela Fan
| Challenge: | Neural machine translation is a natural language deep learning application that needs data to be trained. |
| Approach: | They describe a framework that empowers scalability and versatility for research use cases. |
| Outcome: | The proposed framework empowers scalability and versatility for research use cases. |