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Models/IMN

IMN

Reported on 23 benchmarks across 3 tasks · 2 papers · 13 SOTA

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

Natural Language Processing24 results

Conversational Response SelectiononDouban
MAP· 2019-01-07
0.57
best: 0.651 (SEMSOL(W/o utterances))
SOTA
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots arXiv:1901.01824
Conversational Response SelectiononDouban
MRR· 2019-01-07
0.615
best: 0.688 (Uni-Enc+BERT-FP)
SOTA
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots arXiv:1901.01824
Conversational Response SelectiononDouban
P@1· 2019-01-07
0.433
best: 0.518 (Uni-Enc+BERT-FP)
SOTA
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots arXiv:1901.01824
Conversational Response SelectiononDouban
R10@1· 2019-01-07
0.262
best: 0.33 (SEMSOL)
SOTA
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots arXiv:1901.01824
Conversational Response SelectiononDouban
R10@2· 2019-01-07
0.452
best: 0.557 (Uni-Enc+BERT-FP)
SOTA
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots arXiv:1901.01824
Conversational Response SelectiononDouban
R10@5· 2019-01-07
0.789
best: 0.877 (SEMSOL(W/o utterances))
SOTA
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots arXiv:1901.01824
Conversational Response SelectiononUbuntu Dialogue (v1, Ranking)
R10@1· 2019-01-07
0.794
best: 0.918 (Dial-MAE)
SOTA
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots arXiv:1901.01824
Conversational Response SelectiononUbuntu Dialogue (v1, Ranking)
R10@2· 2019-01-07
0.889
best: 0.965 (BERT-FP+EDHNS)
SOTA
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots arXiv:1901.01824
Conversational Response SelectiononUbuntu Dialogue (v1, Ranking)
R10@5· 2019-01-07
0.974
best: 0.994 (BERT-FP+EDHNS)
SOTA
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots arXiv:1901.01824
Conversational Response SelectiononUbuntu Dialogue (v1, Ranking)
R2@1· 2019-01-07
0.946
best: 0.975 (BERT-SL)
SOTA
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots arXiv:1901.01824
Conversational Response SelectiononE-commerce
R10@1· 2019-01-07
0.621
best: 0.957 (BERT-FP+EDHNS)
SOTA
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots arXiv:1901.01824
Conversational Response SelectiononE-commerce
R10@2· 2019-01-07
0.797
best: 0.986 (BERT-FP+EDHNS)
SOTA
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots arXiv:1901.01824
Conversational Response SelectiononE-commerce
R10@5· 2019-01-07
0.964
best: 0.997 (BERT-FP+EDHNS)
SOTA
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots arXiv:1901.01824
Sentiment AnalysisonSemEval 2014 Task 4 Subtask 1+2
F1· 2019-06-17
58.37
best: 83.76 (gpt-3.5 finetuned)
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis arXiv:1906.06906
Sentiment AnalysisonSemEval-2014 Task-4
Laptop (Acc)· 2019-06-17
75.36
best: 8276 (ABSA-DeBERTa)
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis arXiv:1906.06906
Sentiment AnalysisonSemEval-2014 Task-4
Mean Acc (Restaurant + Laptop)· 2019-06-17
79.63
best: 8611 (ABSA-DeBERTa)
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis arXiv:1906.06906
Sentiment AnalysisonSemEval-2014 Task-4
Restaurant (Acc)· 2019-06-17
83.89
best: 8946 (ABSA-DeBERTa)
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis arXiv:1906.06906
Sentiment AnalysisonSemEval 2014 Task 4 Subtask 1+2
F1· 2019-06-17
58.37
best: 83.76 (gpt-3.5 finetuned)
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis arXiv:1906.06906
Sentiment AnalysisonSemEval 2014 Task 4 Laptop
F1· 2019-06-17
58.37
best: 79.34 (InstructABSA)
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis arXiv:1906.06906
Aspect-Based Sentiment Analysis (ABSA)onSemEval-2014 Task-4
Laptop (Acc)· 2019-06-17
75.36
best: 8276 (ABSA-DeBERTa)
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis arXiv:1906.06906
Aspect-Based Sentiment Analysis (ABSA)onSemEval-2014 Task-4
Mean Acc (Restaurant + Laptop)· 2019-06-17
79.63
best: 8611 (ABSA-DeBERTa)
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis arXiv:1906.06906
Aspect-Based Sentiment Analysis (ABSA)onSemEval-2014 Task-4
Restaurant (Acc)· 2019-06-17
83.89
best: 8946 (ABSA-DeBERTa)
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis arXiv:1906.06906
Aspect-Based Sentiment Analysis (ABSA)onSemEval 2014 Task 4 Subtask 1+2
F1· 2019-06-17
58.37
best: 83.76 (gpt-3.5 finetuned)
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis arXiv:1906.06906
Aspect-Based Sentiment Analysis (ABSA)onSemEval 2014 Task 4 Laptop
F1· 2019-06-17
58.37
best: 79.34 (InstructABSA)
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis arXiv:1906.06906