OpenCodePapers
data-to-text-generation-on-e2e-nlg-challenge
Data-to-Text Generation
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BLEU
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METEOR
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NIST
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ROUGE-L
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CIDEr
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Number of parameters (M)
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ModelName
ReleaseDate
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Pragmatically Informative Text Generation
✓ Link
68.60
45.25
8.73
70.82
2.37
S_1^R
2019-04-02
Copy mechanism and tailored training for character-based data-to-text generation
✓ Link
67.05
44.49
8.5150
68.94
2.2355
EDA_CS
2019-04-26
TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy
66.43
70.14
4.7
TrICy (trK = 0)
2024-01-25
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation
66.19
44.54
8.6130
67.72
Slug
2018-05-16
Findings of the E2E NLG Challenge
✓ Link
65.93
44.83
8.6094
68.50
2.2338
TGen
2018-10-02
Copy mechanism and tailored training for character-based data-to-text generation
✓ Link
65.80
45.16
8.5615
67.40
2.1803
EDA_CS (TL)
2019-04-26
TNT-NLG, System 1: Using a statistical NLG to massively augment crowd-sourced data for neural generation
65.61
45.17
8.5105
68.39
2.2183
Sys1-Primary
2018-04-26
Attention Regularized Sequence-to-Sequence Learning for E2E NLG Challenge
65.45
43.92
8.1804
70.83
2.1012
Zhang
2018-03-01
Self-training from Self-memory in Data-to-text Generation
✓ Link
65.11
46.11
8.35
68.41
2.16
Self-memory
2024-01-19
Technical Report for E2E NLG Challenge
64.22
44.69
8.3453
66.45
2.2721
Gong
2017-12-19
E2E NLG Challenge: Neural Models vs. Templates
✓ Link
56.57
45.29
7.4544
66.14
1.8206
TUDA
2018-11-01