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Multi-turn Response Selection on Ubuntu Corpus

86.9Recall@1 (R10)

BERT + SPIDER

Updated 4d ago

Evaluation Results

Method
BERT + SPIDER 2021.05	86.9	-	93.8	98.7	-	-	-	-
BERT 2021.05	85.7	-	93	98.5	-	-	-	-
SA-BERT 2021.05	85.5	-	92.8	98.3	-	-	-	-
BERT + SPIDER 2021.05	83.1	-	91.3	98	-	-	-	-
TripleNet (Ensemble) 2019.09	82.1	95.6	90.9	98	-	-	-	-
BERT 2021.05	81.7	-	90.4	97.7	-	-	-	-
TripleNet (ELMo) 2019.09	80.5	95.1	89.7	97.6	-	-	-	-
MSN 2021.05	80	-	89.9	97.8	-	-	-	-
IoI 2021.05	79.6	-	89.4	97.4	-	-	-	-
TripleNet 2019.09	79	94.3	88.5	97	-	-	-	-
MRFN 2021.05	78.6	-	88.6	97.6	-	-	-	-
DAM 2019.08	76.7	93.8	-	-	-	-	-	-
DAM 2019.09	76.7	93.8	87.4	96.9	-	-	-	-
DAM 2021.05	76.7	-	87.4	96.9	-	-	-	-
Multi-Granularity (5) 2019.08	75.3	93.45	-	-	84.26	-	-	-
DUA 2018.06	75.2	-	86.8	96.2	-	-	-	-
DUA 2019.09	75.2	-	86.8	96.2	-	-	-	-
DUA 2021.05	75.2	-	86.8	96.2	-	-	-	-
Ensemble (5) 2019.08	74.95	93.27	-	-	84.03	-	-	-
DAM 2019.08	74.54	93.08	-	-	83.74	-	-	-
SMNdynamic 2016.12	72.6	92.6	84.7	96.1	-	-	-	-
SMN 2018.06	72.6	-	84.7	96.1	-	-	-	-
SMN 2019.08	72.6	92.6	-	-	-	-	-	-
SMN 2019.09	72.6	92.6	84.7	96.1	-	-	-	-
SMN 2021.05	72.6	-	84.7	96.1	-	-	-	-
SMNstatic 2016.12	72.5	92.7	83.8	96.2	-	-	-	-
SMNlast 2016.12	72.3	92.3	84.2	95.6	-	-	-	-
Multi-Granularity (5) 2019.08	68.7	91.9	-	-	80.1	-	-	-
RNN-CNN 2019.09	67.2	91.1	80.9	95.6	-	-	-	-
Ensemble (5) 2019.08	66.9	91.7	-	-	78.91	-	-	-
Multi-View 2016.12	66.2	90.8	80.1	95.1	-	-	-	-
Multi-View 2018.06	66.2	-	80.1	95.1	-	-	-	-
Multiview 2019.08	66.2	90.8	-	-	-	-	-	-
Multi-View 2019.09	66.2	90.8	80.1	95.1	-	-	-	-
Multi-Channel 2016.12	65.6	90.4	80.9	94.2	-	-	-	-
Multi-Channel 2018.06	65.6	-	80.9	94.2	-	-	-	-
MV-LSTM 2016.12	65.3	90.6	80.4	94.6	-	-	-	-
Match-LSTM 2016.12	65.3	90.4	79.9	94.4	-	-	-	-
MV-LSTM 2018.06	65.3	-	80.4	94.6	-	-	-	-
Match-LSTM 2018.06	65.3	-	79.9	94.4	-	-	-	-
MV-LSTM 2019.08	65.3	90.6	-	-	-	-	-	-
Match-LSTM 2019.08	65.3	90.4	-	-	-	-	-	-
MV-LSTM 2019.09	65.3	90.6	80.4	94.6	-	-	-	-
Match-LSTM 2019.09	65.3	90.4	80.4	94.6	-	-	-	-
LSTM 2016.12	63.8	90.1	78.4	94.9	-	-	-	-
LSTM 2018.06	63.8	-	78.4	94.9	-	-	-	-
Dual Encoder 2019.08	63.8	90.1	-	-	-	-	-	-
DualEncoder 2019.09	63.8	90.1	78.4	94.9	-	-	-	-
Dual Encoder 2019.08	63.6	90.9	-	-	76.84	-	-	-
Attentive-LSTM 2016.12	63.3	90.3	78.9	94.3	-	-	-	-
Attentive-LSTM 2018.06	63.3	-	78.9	94.3	-	-	-	-
BiLSTM 2016.12	63	89.5	78	94.4	-	-	-	-
BILSTM 2018.06	63	-	78	94.4	-	-	-	-
DL2R 2016.12	62.6	89.9	78.3	94.4	-	-	-	-
DL2R 2018.06	62.6	-	78.3	94.4	-	-	-	-
DL2R 2019.08	62.6	89.9	-	-	-	-	-	-
DL2R 2019.09	62.6	89.9	78.3	94.4	-	-	-	-
CNN 2016.12	54.9	84.8	68.4	89.6	-	-	-	-
CNN 2018.06	54.9	-	68.4	89.6	-	-	-	-
TF-IDF 2016.12	41	65.9	54.5	70.8	-	-	-	-
TF-IDF 2018.06	41	-	54.5	70.8	-	-	-	-
RNN 2016.12	40.3	76.8	54.7	81.9	-	-	-	-
RNN 2018.06	40.3	-	54.7	81.9	-	-	-	-
Multi-Channelexp 2016.12	36.8	71.4	49.7	74.5	-	-	-	-
Multi-Channel_exp 2018.06	36.8	-	49.7	74.5	-	-	-	-
TF-IDF 2019.01	-	-	-	-	-	41	54.5	70.8
RNN 2019.01	-	-	-	-	-	40.3	54.7	81.9
CNN 2019.01	-	-	-	-	-	54.9	68.4	89.6
LSTM 2019.01	-	-	-	-	-	63.8	78.4	94.9
BiLSTM 2019.01	-	-	-	-	-	63	78	94.4
MV-LSTM 2019.01	-	-	-	-	-	65.3	80.4	94.6
Match-LSTM 2019.01	-	-	-	-	-	65.3	79.9	94.4
Attentive-LSTM 2019.01	-	-	-	-	-	63.3	78.9	94.3
Multi-Channel 2019.01	-	-	-	-	-	65.6	80.9	94.2
Multi-View 2019.01	-	-	-	-	-	66.2	80.1	95.1
DL2R 2019.01	-	-	-	-	-	62.6	78.3	94.4
SMN 2019.01	-	-	-	-	-	72.6	84.7	96.1
DUA 2019.01	-	-	-	-	-	75.2	86.8	96.2
DAM 2019.01	-	-	-	-	-	76.7	87.4	96.9
ESIM 2019.01	-	-	-	-	-	79.6	89.4	97.5