Papers › Technical Report for E2E NLG Challenge
Technical Report for E2E NLG Challenge
Heng Gong
This paper describes the primary system submitted by the author to the E2E NLG Challenge on the E2E Dataset (Novikova et al. (2017)). Based on the baseline system called TGen (Dusek and Jurcicek (2016)), the primary system uses REINFORCE to utilize multiple reference for single Meaning Representation during training, while the baseline model treated them as individual training instances.
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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 | Gong | BLEU | 64.22 | #10 of 11 | Archive leaderboard | report |
| Data-to-Text Generation | E2E NLG Challenge | Gong | CIDEr | 2.2721 | #10 of 11 | Archive leaderboard | report |
| Data-to-Text Generation | E2E NLG Challenge | Gong | METEOR | 44.69 | #10 of 11 | Archive leaderboard | report |
| Data-to-Text Generation | E2E NLG Challenge | Gong | NIST | 8.3453 | #10 of 11 | Archive leaderboard | report |
| Data-to-Text Generation | E2E NLG Challenge | Gong | ROUGE-L | 66.45 | #10 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.
Methods
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