Papers by Jacob Bremerman

1 papers
Machine Translation Robustness to Natural Asemantic Variation (2022.emnlp-main)

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Challenge: Existing machine translation models struggle with noisy data and tail-end words and phrases.
Approach: They introduce and formalize a class of noise and variation that preserves meaning in the target language.
Outcome: The proposed model can perform better on natural asemantic variation (NAV) the proposed model is robust to a variety of perturbations, but not all of them are achieved with organic variations.

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