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E2E NLG Challenge: Neural Models vs. Templates
Yevgeniy Puzikov, Iryna Gurevych
E2E NLG Challenge is a shared task on generating restaurant descriptions from sets of key-value pairs. This paper describes the results of our participation in the challenge. We develop a simple, yet effective neural encoder-decoder model which produces fluent restaurant descriptions and outperforms a strong baseline. We further analyze the data provided by the organizers and conclude that the task can also be approached with a template-based model developed in just a few hours.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Data-to-Text Generation | E2E NLG Challenge | TUDA | BLEU | 56.57 | #11 of 11 | Archive leaderboard | report |
| Data-to-Text Generation | E2E NLG Challenge | TUDA | CIDEr | 1.8206 | #11 of 11 | Archive leaderboard | report |
| Data-to-Text Generation | E2E NLG Challenge | TUDA | METEOR | 45.29 | #11 of 11 | Archive leaderboard | report |
| Data-to-Text Generation | E2E NLG Challenge | TUDA | NIST | 7.4544 | #11 of 11 | Archive leaderboard | report |
| Data-to-Text Generation | E2E NLG Challenge | TUDA | ROUGE-L | 66.14 | #11 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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