A surprisingly effective out-of-the-box char2char model on the E2E NLG Challenge dataset

Abstract

We train a char2char model on the E2E NLG Challenge data, by exploiting "out-of-the-box" the recently released tf-seq2seq framework, using some of the standard options of 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.