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Cynthia Gao

4 accepted papers

2025

BOUQuET : dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation

Pierre Andrews, Mikel Artetxe, Mariano Coria Meglioli, Marta R. Costa-juss{\`a}, Joe Chuang, David Dale +13
EMNLP 2025

BOUQuET is a multi-way, multicentric and multi-register/domain dataset and benchmark, and a broader collaborative initiative. This dataset is handcrafted in 8 non-English languages (i.e. Egyptian Arabic and Modern Standard Arabic, French, German, Hindi, Indonesian, Mandarin Chinese, Russian, and Spa

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2023

HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation

David Dale, Elena Voita, Janice Lam, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi +3
EMNLP 2023long main

Hallucinations in machine translation are translations that contain information completely unrelated to the input. Omissions are translations that do not include some of the input information. While both cases tend to be catastrophic errors undermining user trust, annotated data with these types of…

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2023

Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale

Marta R. Costa-jussà, Pierre Andrews, Eric Michael Smith, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi +3
EMNLP 2023long main

We introduce a multilingual extension of the HolisticBias dataset, the largest English template-based taxonomy of textual people references: Multilingual HolisticBias. This extension consists of 20,459 sentences in 50 languages distributed across 13 demographic axes. Source sentences are built from…

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2023

Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation

Jean Maillard, Cynthia Gao, Elahe Kalbassi, Kaushik Ram Sadagopan, Vedanuj Goswami, Philipp Koehn +2
ACL 2023long

For many languages, machine translation progress is hindered by the lack of reliable training data. Models are trained on whatever pre-existing datasets may be available and then augmented with synthetic data, because it is often not economical to pay for the creation of large-scale datasets. But fo…

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