One exception is<cite> (Goyal et al., 2016)</cite> , who employed a char-based seq2seq model where the input MR is simply represented as a character sequence, and the output is also generated char-by-char; this approach avoids the rare word problem, as the character vocabulary is very small. While<cite> (Goyal et al., 2016)</cite> used an additional finite-state mechanism to guide the production of well-formed (and input-motivated) character sequences, the performance of their basic char2char model was already quite good. We further explore how a recent out-of-the box seq2seq model would perform on E2E NLG Challenge, when used in a char-based mode.