Papers by Ana Guerberof-Arenas
DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages (2022.emnlp-main)
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| Challenge: | Recent advances in neural language modeling and multilingual training have prompted widespread adoption of machine translation (MT) technologies across an unprecedented range of world languages. |
| Approach: | They propose to use a dataset to assess the impact of two state-of-the-art NMT systems, Google Translate and the multilingual mBART-50 model, on translation productivity. |
| Outcome: | The proposed model is faster than translation from scratch, but the magnitude of productivity gains varies widely across systems and languages. |
What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study (2024.emnlp-main)
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| Challenge: | Existing bias measurements do not reflect the gender disparities found in machine translation. |
| Approach: | They conduct a human-centered study to examine if and to what extent bias in machine translation brings harms with tangible costs, such as quality of service gaps between women and men. |
| Outcome: | The findings advocate for human-centered approaches that can inform the societal impact of bias. |