HyperAI

Conversational Response Selection On Ubuntu 1

Metriken

R10@1
R10@2
R10@5

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Vergleichstabelle
ModellnameR10@1R10@2R10@5
fine-grained-post-training-for-improving0.9110.9620.994
sequential-attention-based-network-for-noetic0.7960.8940.975
1905019690.8820.9490.990
multi-turn-response-selection-for-chatbots0.7670.8740.969
global-selector-a-new-benchmark-dataset-and0.9160.9650.994
response-ranking-with-multi-types-of-deep0.8210.9110.981
multi-view-response-selection-for-human0.6620.8010.951
modeling-multi-turn-conversation-with-deep0.7520.8680.962
contextual-masked-auto-encoder-for-retrieval0.9180.9640.993
the-ubuntu-dialogue-corpus-a-large-dataset-10.6040.7450.926
do-response-selection-models-really-know-what0.8750.9420.988
triplenet-triple-attention-network-for-multi0.7900.8850.970
sampling-matters-an-empirical-study-of0.7850.8830.974
learning-an-effective-context-response0.8840.9460.990
multi-hop-selector-network-for-multi-turn0.8000.8990.978
sequential-matching-network-a-new0.7260.8220.960
efficient-dynamic-hard-negative-sampling-for0.9170.9650.994
multi-granularity-representations-of-dialog0.753--
domain-adaptive-training-bert-for-response0.8550.9280.985
global-selector-a-new-benchmark-dataset-and0.8860.9460.989
speaker-aware-bert-for-multi-turn-response0.8550.9280.983
small-changes-make-big-differences-improving0.8860.9480.990
interactive-matching-network-for-multi-turn0.7940.8890.974
improved-deep-learning-baselines-for-ubuntu0.6300.7800.944
one-time-of-interaction-may-not-be-enough-go0.7960.8940.974