Papers › A surprisingly effective out-of-the-box char2char model on the E2E NLG Challenge dataset
A surprisingly effective out-of-the-box char2char model on the E2E NLG Challenge dataset
Shubham Agarwal, Marc Dymetman
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.
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