@InProceedings{agarwal-dymetman:2017:W17-55,
  author    = {Agarwal, Shubham  and  Dymetman, Marc},
  title     = {A surprisingly effective out-of-the-box char2char model on the E2E NLG Challenge dataset},
  booktitle = {Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue},
  month     = {August},
  year      = {2017},
  address   = {Saarbrücken, Germany},
  publisher = {Association for Computational Linguistics},
  pages     = {158--163},
  abstract  = {We train a char2char model on the E2E NLG Challenge data, by exploiting
	“out-of-the-box” the recently released tfseq2seq framework, using some of
	the standard options offered by this tool. With minimal effort, and in
	particular without delexicalization, tokenization or lowercasing, the obtained
	raw predictions, according to a small scale human evaluation, are excellent on
	the linguistic side and quite reasonable on the adequacy side, the primary
	downside being the possible omissions of semantic material. However, in a
	significant number of cases (more than 70%), a perfect solution can be found in
	the top-20 predictions, indicating promising directions for solving the
	remaining issues.},
  url       = {http://aclweb.org/anthology/W17-5519}
}