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- W3028938056 abstract "Recent works have shown that Neural Machine Translation (NMT) models achieve impressive performance, however, questions about understanding the behavior of these models remain unanswered. We investigate the unexpected volatility of NMT models where the input is semantically and syntactically correct. We discover that with trivial modifications of source sentences, we can identify cases where textit{unexpected changes} happen in the translation and in the worst case lead to mistranslations. This volatile behavior of translating extremely similar sentences in surprisingly different ways highlights the underlying generalization problem of current NMT models. We find that both RNN and Transformer models display volatile behavior in 26% and 19% of sentence variations, respectively." @default.
- W3028938056 created "2020-06-05" @default.
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- W3028938056 date "2020-05-25" @default.
- W3028938056 modified "2023-09-23" @default.
- W3028938056 title "The Unreasonable Volatility of Neural Machine Translation Models" @default.
- W3028938056 hasPublicationYear "2020" @default.
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