Publication: Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks
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Emelin, D., Titov, I., & Sennrich, R. (2020). Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks. 7635–7653. https://www.aclweb.org/anthology/2020.emnlp-main.616
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Word sense disambiguation is a well-known source of translation errors in NMT. We posit that some of the incorrect disambiguation choices are due to models' over-reliance on dataset artifacts found in training data, specifically superficial word co-occurrences, rather than a deeper understanding of the source text. We introduce a method for the prediction of disambiguation errors based on statistical data properties, demonstrating its effectiveness across several domains and model types. Moreover, we develop a simple adversarial attac
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Emelin, D., Titov, I., & Sennrich, R. (2020). Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks. 7635–7653. https://www.aclweb.org/anthology/2020.emnlp-main.616
