Papers › Findings of the E2E NLG Challenge
Findings of the E2E NLG Challenge
Ondřej Dušek, Jekaterina Novikova, Verena Rieser
This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems. Recent end-to-end generation systems are promising since they reduce the need for data annotation. However, they are currently limited to small, delexicalised datasets. The E2E NLG shared task aims to assess whether these novel approaches can generate better-quality output by learning from a dataset containing higher lexical richness, syntactic complexity and diverse discourse phenomena. We compare 62 systems submitted by 17 institutions, covering a wide range of approaches, including machine learning architectures -- with the majority implementing sequence-to-sequence models (seq2seq) -- as well as systems based on grammatical rules and templates.
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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 | TGen | BLEU | 65.93 | #5 of 11 | Archive leaderboard | report |
| Data-to-Text Generation | E2E NLG Challenge | TGen | CIDEr | 2.2338 | #5 of 11 | Archive leaderboard | report |
| Data-to-Text Generation | E2E NLG Challenge | TGen | METEOR | 44.83 | #5 of 11 | Archive leaderboard | report |
| Data-to-Text Generation | E2E NLG Challenge | TGen | NIST | 8.6094 | #5 of 11 | Archive leaderboard | report |
| Data-to-Text Generation | E2E NLG Challenge | TGen | ROUGE-L | 68.50 | #5 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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