Using the described approach, we demonstrate a word-error-rate (WER) reduction of 65% over a do-nothing input baseline, and we improve over a state-of-the-art system<cite> (Eskander et al., 2013)</cite> which relies heavily on language-specific and manually-selected constraints. We present a detailed analysis of mistakes and demonstrate that the proposed model indeed learns to correct a wider variety of errors.