The fact that we use deletion and insertion also explains why our model was able to regain a significant portion of its original performance when confronted with natural noise at test time, while <cite>previous work</cite> that trained only on substitutions and swaps was not able to do so <cite>(Belinkov and Bisk, 2018)</cite> .