  This paper describes our submission to the E2E NLG Challenge. Recently,
neural seq2seq approaches have become mainstream in NLG, often resorting to
pre- (respectively post-) processing delexicalization (relexicalization) steps
at the word-level to handle rare words. By contrast, we train a simple
character level seq2seq model, which requires no pre/post-processing
(delexicalization, tokenization or even lowercasing), with surprisingly good
results. For further improvement, we explore two re-ranking approaches for
scoring candidates. We also introduce a synthetic dataset creation procedure,
which opens up a new way of creating artificial datasets for Natural Language
Generation.
