Papers by Cynthia Gao
The Flores-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation (2022.tacl-1)
Copied to clipboard
Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzmán, Angela Fan
| Challenge: | a lack of good evaluation benchmarks hinders progress in low-resource and multilingual machine translation . despite advances in translation quality for a handful of languages, many low-source languages are not even supported by most popular translation engines. |
| Approach: | They propose a high-quality evaluation benchmark for machine translation using 3001 sentences from Wikipedia . they aim to improve evaluation of models on long tail of low-resource languages . |
| Outcome: | The proposed evaluation benchmarks are based on 3001 sentences extracted from Wikipedia . the results show that the models can be used to evaluate multilingual systems . |
BOUQuET : dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation (2025.emnlp-main)
Copied to clipboard
Pierre Andrews, Mikel Artetxe, Mariano Coria Meglioli, Marta R. Costa-jussà, Joe Chuang, David Dale, Mark Duppenthaler, Nathanial Paul Ekberg, Cynthia Gao, Daniel Edward Licht, Jean Maillard, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Eduardo Sánchez, Ioannis Tsiamas, Arina Turkatenko, Albert Ventayol-Boada, Shireen Yates
| Challenge: | BOUQUET is a multi-way, multicentric and multi-register/domain dataset and benchmark . the dataset is handcrafted in 8 non-English languages . |
| Approach: | They propose to use BOUQuET to collect a multi-way, multicentric and multi-register/domain dataset and benchmark in 8 non-English languages. |
| Outcome: | The proposed dataset is available at https://huggingface.co/datasets/facebook/bouquet. |
Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale (2023.emnlp-main)
Copied to clipboard
Marta Costa-jussà, Pierre Andrews, Eric Smith, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi, Cynthia Gao, Daniel Licht, Carleigh Wood
| Challenge: | Multilingual HolisticBias dataset includes 20,459 sentences in 50 languages . dataset is intended to uncover demographic imbalances and quantify mitigations . |
| Approach: | They propose a multilingual extension of the HolisticBias dataset . they use 118 demographic descriptors and three patterns to build multilingual sentences . |
| Outcome: | The proposed model improves translation quality when the source input only differs in gender . it also improves when the masculine human reference is used in the model . |
HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation (2023.emnlp-main)
Copied to clipboard
David Dale, Elena Voita, Janice Lam, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi, Cynthia Gao, Loic Barrault, Marta Costa-jussà
| Challenge: | Previously available quality assessments do not distinguish between hallucinations and omissions. |
| Approach: | They propose to annotate hallucinations and omissions in machine translation using a single language pair. |
| Outcome: | The proposed dataset covers 18 translation directions with varying resource levels and scripts. |
Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation (2023.acl-long)
Copied to clipboard
Jean Maillard, Cynthia Gao, Elahe Kalbassi, Kaushik Ram Sadagopan, Vedanuj Goswami, Philipp Koehn, Angela Fan, Francisco Guzman
| Challenge: | Existing datasets are not economical to create large-scale datasets, but for low-resource languages, a few thousand professionally translated sentence pairs can be useful. |
| Approach: | They propose to use a dataset to train machine translation models on pre-existing and synthetic data to augment them with millions of sentences through backtranslation. |
| Outcome: | The proposed model can cover hundreds of languages with high quality training data even when smaller but lower quality datasets are used. |