MTNT: A Testbed for Machine Translation of Noisy Text
This provides a testbed for developing noise-robust machine translation systems, addressing a critical issue for applications handling user-generated content, though it is incremental as it focuses on dataset creation.
The authors tackled the lack of publicly available parallel corpora for noisy text in machine translation by creating the MTNT benchmark dataset, which includes 7k-37k sentences per language pair from Reddit comments and professional translations, and showed that existing MT models perform poorly on this data even after adaptation.
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/.