Papers by Elahe Kalbassi
Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale (2023.emnlp-main)
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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 . |
MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector (2024.findings-acl)
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Marta Costa-jussà, Mariano Meglioli, Pierre Andrews, David Dale, Prangthip Hansanti, Elahe Kalbassi, Alexandre Mourachko, Christophe Ropers, Carleigh Wood
| Challenge: | Existing studies on text-based toxicity detection for other languages are limited, especially for languages other than English. |
| Approach: | They propose a multilingual audio-based toxicity classifier which covers 14 different linguistic families and a dataset of 20,000 audio utterances for English and Spanish. |
| Outcome: | The new classifier improves F1-Score by an average of 100% when compared to existing wordlist-based classifiers. |
HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation (2023.emnlp-main)
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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)
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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. |