Detecting Word Sense Disambiguation Biases in Machine Translation for Model Agnostic Adversarial Attacks

Denis Emelin
Denis Emelin

EMNLP 2020, 2020.

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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

Abstract:

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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