Detecting Word Sense Disambiguation Biases in Machine Translation for Model Agnostic Adversarial Attacks
EMNLP 2020, 2020.
We conducted an initial investigation into leveraging data artifacts for the prediction of word sense disambiguation errors in machine translation and proposed a simple adversarial attack strategy based on the presented insights
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 f...More
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