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Models/Poly-encoder

Poly-encoder

Reported on 11 benchmarks across 1 task · 1 paper · 11 SOTA

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

Natural Language Processing11 results

Conversational Response SelectiononDouban
MAP· 2019-04-22
0.608
best: 0.651 (SEMSOL(W/o utterances))
SOTA
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring arXiv:1905.01969
Conversational Response SelectiononDouban
MRR· 2019-04-22
0.65
best: 0.688 (Uni-Enc+BERT-FP)
SOTA
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring arXiv:1905.01969
Conversational Response SelectiononDouban
P@1· 2019-04-22
0.475
best: 0.518 (Uni-Enc+BERT-FP)
SOTA
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring arXiv:1905.01969
Conversational Response SelectiononDouban
R10@1· 2019-04-22
0.299
best: 0.33 (SEMSOL)
SOTA
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring arXiv:1905.01969
Conversational Response SelectiononDouban
R10@2· 2019-04-22
0.494
best: 0.557 (Uni-Enc+BERT-FP)
SOTA
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring arXiv:1905.01969
Conversational Response SelectiononDouban
R10@5· 2019-04-22
0.822
best: 0.877 (SEMSOL(W/o utterances))
SOTA
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring arXiv:1905.01969
Conversational Response SelectiononRRS Ranking Test
NDCG@3· 2019-04-22
0.679
SOTA
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring arXiv:1905.01969
Conversational Response SelectiononRRS Ranking Test
NDCG@5· 2019-04-22
0.765
SOTA
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring arXiv:1905.01969
Conversational Response SelectiononUbuntu Dialogue (v1, Ranking)
R10@1· 2019-04-22
0.882
best: 0.918 (Dial-MAE)
SOTA
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring arXiv:1905.01969
Conversational Response SelectiononUbuntu Dialogue (v1, Ranking)
R10@2· 2019-04-22
0.949
best: 0.965 (BERT-FP+EDHNS)
SOTA
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring arXiv:1905.01969
Conversational Response SelectiononUbuntu Dialogue (v1, Ranking)
R10@5· 2019-04-22
0.99
best: 0.994 (BERT-FP+EDHNS)
SOTA
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring arXiv:1905.01969