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Models/TGen

TGen

Reported on 15 benchmarks across 2 tasks · 3 papers · 8 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Adversarial8 results

Text GenerationonCleaned E2E NLG Challenge
BLEU (Test set)· 2019-11-10
40.73
best: 44.15 (Control Prefixes (T5-large))
SOTA
Semantic Noise Matters for Neural Natural Language Generation arXiv:1911.03905
Text GenerationonE2E NLG Challenge
CIDEr· 2018-10-02
2.2338
best: 2.37 (S_1^R)
SOTA
Findings of the E2E NLG Challenge arXiv:1810.01170
Text GenerationonE2E NLG Challenge
METEOR· 2018-10-02
44.83
best: 46.11 (Self-memory)
SOTA
Findings of the E2E NLG Challenge arXiv:1810.01170
Text GenerationonE2E NLG Challenge
ROUGE-L· 2018-10-02
68.5
best: 70.83 (Zhang)
SOTA
Findings of the E2E NLG Challenge arXiv:1810.01170
Text GenerationonCzech restaurant information
METEOR· 2021-02-02
0.152
best: 0.167 (TGen++)
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics arXiv:2102.01672
Text GenerationonCleaned E2E NLG Challenge
METEOR (Validation set)· 2021-02-02
0.391
best: 0.394 (LSTM)
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics arXiv:2102.01672
Text GenerationonE2E NLG Challenge
BLEU· 2018-10-02
65.93
best: 68.6 (S_1^R)
Findings of the E2E NLG Challenge arXiv:1810.01170
Text GenerationonE2E NLG Challenge
NIST· 2018-10-02
8.6094
best: 8.73 (S_1^R)
Findings of the E2E NLG Challenge arXiv:1810.01170

Natural Language Processing7 results

Data-to-Text GenerationonCleaned E2E NLG Challenge
BLEU (Test set)· 2019-11-10
40.73
best: 44.15 (Control Prefixes (T5-large))
SOTA
Semantic Noise Matters for Neural Natural Language Generation arXiv:1911.03905
Data-to-Text GenerationonE2E NLG Challenge
CIDEr· 2018-10-02
2.2338
best: 2.37 (S_1^R)
SOTA
Findings of the E2E NLG Challenge arXiv:1810.01170
Data-to-Text GenerationonE2E NLG Challenge
METEOR· 2018-10-02
44.83
best: 46.11 (Self-memory)
SOTA
Findings of the E2E NLG Challenge arXiv:1810.01170
Data-to-Text GenerationonE2E NLG Challenge
ROUGE-L· 2018-10-02
68.5
best: 70.83 (Zhang)
SOTA
Findings of the E2E NLG Challenge arXiv:1810.01170
Data-to-Text GenerationonCleaned E2E NLG Challenge
METEOR (Validation set)· 2021-02-02
0.391
best: 0.394 (LSTM)
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics arXiv:2102.01672
Data-to-Text GenerationonE2E NLG Challenge
BLEU· 2018-10-02
65.93
best: 68.6 (S_1^R)
Findings of the E2E NLG Challenge arXiv:1810.01170
Data-to-Text GenerationonE2E NLG Challenge
NIST· 2018-10-02
8.6094
best: 8.73 (S_1^R)
Findings of the E2E NLG Challenge arXiv:1810.01170