Papers by Xavier Garcia
FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation (2023.tacl-1)
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Parker Riley, Timothy Dozat, Jan A. Botha, Xavier Garcia, Dan Garrette, Jason Riesa, Orhan Firat, Noah Constant
| Challenge: | a new dataset and evaluation benchmark for Few-shot Region-aware Machine Translation is presented . FRMT is a type of style-targeted translation that uses labeled training data to perform tasks. |
| Approach: | They propose a dataset and evaluation benchmark for Few-shot Region-aware Machine Translation. |
| Outcome: | The proposed model is based on two translations from English into Portuguese and Mandarin Chinese. |
A Multilingual View of Unsupervised Machine Translation (2020.findings-emnlp)
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| Challenge: | Empirically, we show that our approach results in higher BLEU scores over state-of-the-art unsupervised models on the WMT’14 English-French, WMT'16 English-German, and WMT‘16 English–Romanian datasets in most directions. |
| Approach: | They propose a probabilistic framework for multilingual neural machine translation that encompasses supervised and unsupervised setups, focusing on unsupervised translation. |
| Outcome: | The proposed framework achieves higher BLEU scores than state-of-the-art unsupervised models on the WMT’14 English-French, WMT'16 English-German, and WMT‘16 English–Romanian datasets in most directions. |
Transcending Scaling Laws with 0.1% Extra Compute (2023.emnlp-main)
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Yi Tay, Jason Wei, Hyung Chung, Vinh Tran, David So, Siamak Shakeri, Xavier Garcia, Steven Zheng, Jinfeng Rao, Aakanksha Chowdhery, Denny Zhou, Donald Metzler, Slav Petrov, Neil Houlsby, Quoc Le, Mostafa Dehghani
| Challenge: | Existing scaling of language models is expensive and requires significant computational costs. |
| Approach: | They propose a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
| Outcome: | The proposed method significantly improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages (2021.naacl-main)
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| Challenge: | Unsupervised translation systems have impressive performance on resource-rich language pairs . however, in more realistic settings, unsupervised systems perform poorly . |
| Approach: | They propose a model for 5 low-resource languages that leverages monolingual and auxiliary parallel data from other high-resourced languages. |
| Outcome: | The proposed model outperforms state-of-the-art models on low-resource languages . it also matches the current state- of-the art model for Nepali-English . |
Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution (2021.naacl-main)
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| Challenge: | Existing approaches to multilingual machine translation rely on training models on monolingual data for all languages in a multitask setup. |
| Approach: | They propose a vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models by combining monolingual data with a dictionary. |
| Outcome: | The proposed model improves on existing models by preserving the original model and allowing for competitive performance even with only monolingual data. |
Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings (2022.acl-long)
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| Challenge: | Existing methods for few-shot style transfer often copy inputs verbatim . a new method is better at controlling the style transfer magnitude using an input scalar knob. |
| Approach: | They propose a method to model the stylistic difference between paraphrases by rewriting a sentence into a target style while preserving semantics. |
| Outcome: | The proposed method achieves 2-3x better performance in formality transfer and code-mixing addition across seven languages. |