MTNT: A Testbed for Machine Translation of Noisy Text
EMNLP, pp. 543-553, 2018.
We proposed a new dataset to test Machine Translation models for robustness to the types of noise encountered in natural language on the Internet
Noisy or non-standard input text can cause disastrous mistranslations in most modern Machine Translation (MT) systems, and there has been growing research interest in creating noise-robust MT systems. However, as of yet there are no publicly available parallel corpora of with naturally occurring noisy inputs and translations, and thus pre...More
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