Onur Çelebi

Also published as: Onur Celebi


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xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languages
Mingda Chen | Kevin Heffernan | Onur Çelebi | Alexandre Mourachko | Holger Schwenk
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We introduce a new proxy score for evaluating bitext mining based on similarity in a multilingual embedding space: xsim++. In comparison to xsim, this improved proxy leverages rule-based approaches to extend English sentences in any evaluation set with synthetic, hard-to-distinguish examples which more closely mirror the scenarios we encounter during large-scale mining. We validate this proxy by running a significant number of bitext mining experiments for a set of low-resource languages, and subsequently train NMT systems on the mined data. In comparison to xsim, we show that xsim++ is better correlated with the downstream BLEU scores of translation systems trained on mined bitexts, providing a reliable proxy of bitext mining performance without needing to run expensive bitext mining pipelines. xsim++ also reports performance for different error types, offering more fine-grained feedbacks for model development.


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stopes - Modular Machine Translation Pipelines
Pierre Andrews | Guillaume Wenzek | Kevin Heffernan | Onur Çelebi | Anna Sun | Ammar Kamran | Yingzhe Guo | Alexandre Mourachko | Holger Schwenk | Angela Fan
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Neural machine translation, as other natural language deep learning applications, is hungry for data. As research evolves, the data pipelines supporting that research evolve too, oftentimes re-implementing the same core components. Despite the potential of modular codebases, researchers have but little time to put code structure and reusability first. Unfortunately, this makes it very hard to publish clean, reproducible code to benefit a wider audience. In this paper, we motivate and describe stopes , a framework that addresses these issues while empowering scalability and versatility for research use cases. This library was a key enabler of the No Language Left Behind project, establishing new state of the art performance for a multilingual machine translation model covering 200 languages. stopes and the pipelines described are released under the MIT license at https://github.com/facebookresearch/stopes.

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Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
Kevin Heffernan | Onur Çelebi | Holger Schwenk
Findings of the Association for Computational Linguistics: EMNLP 2022

Scaling multilingual representation learning beyond the hundred most frequent languages is challenging, in particular to cover the long tail of low-resource languages. We move away from the popular one-for-all multilingual models and focus on training multiple language (family) specific representations, but most prominently enable all languages to still be encoded in the same representational space. We focus on teacher-student training, allowing all encoders to be mutually compatible for bitext mining, and enabling fast learning of new languages. We also combine supervised and self-supervised training, allowing encoders to take advantage of monolingual training data.Our approach significantly outperforms the original LASER encoder. We study very low-resource languages and handle 44 African languages, many of which are not covered by any other model. For these languages, we train sentence encoders and mine bitexts. Adding these mined bitexts yielded an improvement of 3.8 BLEU for NMT into English.


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Self-training Improves Pre-training for Natural Language Understanding
Jingfei Du | Edouard Grave | Beliz Gunel | Vishrav Chaudhary | Onur Celebi | Michael Auli | Veselin Stoyanov | Alexis Conneau
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Unsupervised pre-training has led to much recent progress in natural language understanding. In this paper, we study self-training as another way to leverage unlabeled data through semi-supervised learning. To obtain additional data for a specific task, we introduce SentAugment, a data augmentation method which computes task-specific query embeddings from labeled data to retrieve sentences from a bank of billions of unlabeled sentences crawled from the web. Unlike previous semi-supervised methods, our approach does not require in-domain unlabeled data and is therefore more generally applicable. Experiments show that self-training is complementary to strong RoBERTa baselines on a variety of tasks. Our augmentation approach leads to scalable and effective self-training with improvements of up to 2.6% on standard text classification benchmarks. Finally, we also show strong gains on knowledge-distillation and few-shot learning.