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

저자
Shubham Agarwal, Marc Dymetman
인용
SIGDIAL 2017 Special Session: Natural Language Generation for Dialogue Systems, Saarbruecken, Germany, 15 - 17 August 2017
초록

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 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.

발행년도
2017
파일 다운로드
A surprisingly effective out-of-the-box char2char model on the E2E NLG Challenge dataset.pdf (0.26MB)