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الرئيسية
SOTA
Data To Text Generation
Data To Text Generation On E2E Nlg Challenge
Data To Text Generation On E2E Nlg Challenge
المقاييس
BLEU
CIDEr
METEOR
NIST
ROUGE-L
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
BLEU
CIDEr
METEOR
NIST
ROUGE-L
Paper Title
Repository
Gong
64.22
2.2721
44.69
8.3453
66.45
Technical Report for E2E NLG Challenge
-
TrICy (trK = 0)
66.43
-
-
-
70.14
TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy
-
Sys1-Primary
65.61
2.2183
45.17
8.5105
68.39
TNT-NLG, System 1: Using a statistical NLG to massively augment crowd-sourced data for neural generation
-
TGen
65.93
2.2338
44.83
8.6094
68.50
Findings of the E2E NLG Challenge
EDA_CS
67.05
2.2355
44.49
8.5150
68.94
Copy mechanism and tailored training for character-based data-to-text generation
TUDA
56.57
1.8206
45.29
7.4544
66.14
E2E NLG Challenge: Neural Models vs. Templates
Slug
66.19
-
44.54
8.6130
67.72
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation
-
Self-memory
65.11
2.16
46.11
8.35
68.41
Self-training from Self-memory in Data-to-text Generation
EDA_CS (TL)
65.80
2.1803
45.16
8.5615
67.40
Copy mechanism and tailored training for character-based data-to-text generation
Zhang
65.45
2.1012
43.92
8.1804
70.83
Attention Regularized Sequence-to-Sequence Learning for E2E NLG Challenge
-
S_1^R
68.60
2.37
45.25
8.73
70.82
Pragmatically Informative Text Generation
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