| SEMSOL(W/o utterances) |
- MAP
- 0.651
- MRR
- 0.687
- P@1
- 0.510
- R10@1
- 0.328
- R10@2
- 0.552
- R10@5
- 0.877
|
Knowledge-aware response selection with semantics underlying multi-turn open-domain conversations
2023-07-27 |
코드 1 |
| Uni-Enc+BERT-FP |
- MAP
- 0.648
- MRR
- 0.688
- P@1
- 0.518
- R10@1
- 0.327
- R10@2
- 0.557
- R10@5
- 0.865
|
Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems
2021-06-02 |
코드 1 |
| BERT-FP |
- MAP
- 0.644
- MRR
- 0.680
- P@1
- 0.512
- R10@1
- 0.324
- R10@2
- 0.542
- R10@5
- 0.870
|
Fine-grained Post-training for Improving Retrieval-based Dialogue Systems
2021-05-24 |
코드 1 |
| SEMSOL |
- MAP
- 0.640
- MRR
- 0.678
- P@1
- 0.511
- R10@1
- 0.330
- R10@2
- 0.520
- R10@5
- 0.870
|
Knowledge-aware response selection with semantics underlying multi-turn open-domain conversations
2023-07-27 |
코드 1 |
| SA-BERT+HCL |
- MAP
- 0.639
- MRR
- 0.681
- P@1
- 0.514
- R10@1
- 0.330
- R10@2
- 0.531
- R10@5
- 0.858
|
Dialogue Response Selection with Hierarchical Curriculum Learning
2020-12-29 |
코드 1 |
| UMS_BERT+ |
- MAP
- 0.625
- MRR
- 0.664
- P@1
- 0.499
- R10@1
- 0.318
- R10@2
- 0.482
- R10@5
- 0.858
|
Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection
2020-09-10 |
코드 1 |
| Uni-Encoder |
- MAP
- 0.622
- MRR
- 0.662
- P@1
- 0.481
- R10@1
- 0.303
- R10@2
- 0.514
- R10@5
- 0.852
|
Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems
2021-06-02 |
코드 1 |
| SA-BERT |
- MAP
- 0.619
- MRR
- 0.659
- P@1
- 0.496
- R10@1
- 0.313
- R10@2
- 0.481
- R10@5
- 0.847
|
Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots
2020-04-07 |
코드 1 · 코드 2 |
| Poly-encoder |
- MAP
- 0.608
- MRR
- 0.650
- P@1
- 0.475
- R10@1
- 0.299
- R10@2
- 0.494
- R10@5
- 0.822
|
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
2019-04-22 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 |
| BERT |
- MAP
- 0.591
- MRR
- 0.633
- P@1
- 0.454
- R10@1
- 0.280
- R10@2
- 0.470
- R10@5
- 0.828
|
An Effective Domain Adaptive Post-Training Method for BERT in Response Selection
2019-08-13 |
코드 1 |
| MSN |
- MAP
- 0.587
- MRR
- 0.632
- P@1
- 0.470
- R10@1
- 0.295
- R10@2
- 0.452
- R10@5
- 0.788
|
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots
2019-11-01 |
코드 1 |
| IoI |
- MAP
- 0.573
- MRR
- 0.621
- P@1
- 0.444
- R10@1
- 0.269
- R10@2
- 0.451
- R10@5
- 0.786
|
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues
2019-07-01 |
코드 1 |
| IMN |
- MAP
- 0.570
- MRR
- 0.615
- P@1
- 0.433
- R10@1
- 0.262
- R10@2
- 0.452
- R10@5
- 0.789
|
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
2019-01-07 |
코드 1 |
| DUA |
- MAP
- 0.551
- MRR
- 0.599
- P@1
- 0.421
- R10@1
- 0.243
- R10@2
- 0.421
- R10@5
- 0.780
|
Modeling Multi-turn Conversation with Deep Utterance Aggregation
2018-06-24 |
코드 1 |
| DAM |
- MAP
- 0.550
- MRR
- 0.601
- P@1
- 0.427
- R10@1
- 0.254
- R10@2
- 0.410
- R10@5
- 0.757
|
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network
2018-07-01 |
코드 1 |
| SMN |
- MAP
- 0.529
- MRR
- 0.569
- P@1
- 0.397
- R10@1
- 0.233
- R10@2
- 0.396
- R10@5
- 0.724
|
Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots
2016-12-06 |
코드 1 · 코드 2 · 코드 3 |