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Data To Text Generation On E2E Nlg Challenge

Metrics

BLEU
CIDEr
METEOR
NIST
ROUGE-L

Results

Performance results of various models on this benchmark

Paper Title
S_1^R68.602.3745.258.7370.82Pragmatically Informative Text Generation
EDA_CS67.052.235544.498.515068.94Copy mechanism and tailored training for character-based data-to-text generation
TrICy (trK = 0)66.43---70.14TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy
Slug66.19-44.548.613067.72A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation
TGen65.932.233844.838.609468.50Findings of the E2E NLG Challenge
EDA_CS (TL)65.802.180345.168.561567.40Copy mechanism and tailored training for character-based data-to-text generation
Sys1-Primary65.612.218345.178.510568.39TNT-NLG, System 1: Using a statistical NLG to massively augment crowd-sourced data for neural generation
Zhang65.452.101243.928.180470.83Attention Regularized Sequence-to-Sequence Learning for E2E NLG Challenge
Self-memory65.112.1646.118.3568.41Self-training from Self-memory in Data-to-text Generation
Gong64.222.272144.698.345366.45Technical Report for E2E NLG Challenge
TUDA56.571.820645.297.454466.14E2E NLG Challenge: Neural Models vs. Templates
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Data To Text Generation On E2E Nlg Challenge | SOTA | HyperAI