  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 previous work has resorted to evaluating on
synthetically created datasets. In this paper, we propose a benchmark dataset
for Machine Translation of Noisy Text (MTNT), consisting of noisy comments on
Reddit (www.reddit.com) and professionally sourced translations. We
commissioned translations of English comments into French and Japanese, as well
as French and Japanese comments into English, on the order of 7k-37k sentences
per language pair. We qualitatively and quantitatively examine the types of
noise included in this dataset, then demonstrate that existing MT models fail
badly on a number of noise-related phenomena, even after performing adaptation
on a small training set of in-domain data. This indicates that this dataset can
provide an attractive testbed for methods tailored to handling noisy text in
MT. The data is publicly available at www.cs.cmu.edu/~pmichel1/mtnt/.
