Natural Language Inference--自然语言推理

自然语言推理是推断两个句子之间的关系，比如一个是前提的句子和一个是假设的句子，他们的关系可以是蕴涵，中立和矛盾。

数据集：The Stanford Natural Language Inference (SNLI) Corpus

https://nlp.stanford.edu/projects/snli/

发展状况：

Publication	Model	Parameters	Train (% acc)	Test (% acc)

Feature-based models

Bowman et al. '15	Unlexicalized features		49.4	50.4
Bowman et al. '15	+ Unigram and bigram features		99.7	78.2

Sentence vector-based models

Bowman et al. '15	100D LSTM encoders	220k	84.8	77.6
Bowman et al. '16	300D LSTM encoders	3.0m	83.9	80.6
Vendrov et al. '15	1024D GRU encoders w/ unsupervised 'skip-thoughts' pre-training	15m	98.8	81.4
Mou et al. '15	300D Tree-based CNN encoders	3.5m	83.3	82.1
Bowman et al. '16	300D SPINN-PI encoders	3.7m	89.2	83.2
Yang Liu et al. '16	600D (300+300) BiLSTM encoders	2.0m	86.4	83.3
Munkhdalai & Yu '16b	300D NTI-SLSTM-LSTM encoders	4.0m	82.5	83.4
Yang Liu et al. '16	600D (300+300) BiLSTM encoders with intra-attention	2.8m	84.5	84.2
Conneau et al. '17	4096D BiLSTM with max-pooling	40m	85.6	84.5
Munkhdalai & Yu '16a	300D NSE encoders	3.0m	86.2	84.6
Qian Chen et al. '17	600D (300+300) Deep Gated Attn. BiLSTM encoders (code)	12m	90.5	85.5
Tao Shen et al. '17	300D Directional self-attention network encoders (code)	2.4m	91.1	85.6
Jihun Choi et al. '17	300D Gumbel TreeLSTM encoders	2.9m	91.2	85.6
Nie and Bansal '17	300D Residual stacked encoders	9.7m	89.8	85.7
Anonymous '18	1200D REGMAPR (Base+Reg)	–	–	85.9
Yi Tay et al. '18	300D CAFE (no cross-sentence attention)	3.7m	87.3	85.9
Jihun Choi et al. '17	600D Gumbel TreeLSTM encoders	10m	93.1	86.0
Nie and Bansal '17	600D Residual stacked encoders	29m	91.0	86.0
Tao Shen et al. '18	300D Reinforced Self-Attention Network	3.1m	92.6	86.3
Im and Cho '17	Distance-based Self-Attention Network	4.7m	89.6	86.3
Seonhoon Kim et al. '18	Densely-Connected Recurrent and Co-Attentive Network (encoder)	5.6m	91.4	86.5
Talman et al. '18	600D Hierarchical BiLSTM with Max Pooling (HBMP, code)	22m	89.9	86.6
Qian Chen et al. '18	600D BiLSTM with generalized pooling	65m	94.9	86.6
Kiela et al. '18	512D Dynamic Meta-Embeddings	9m	91.6	86.7
Deunsol Yoon et al. '18	600D Dynamic Self-Attention Model	2.1m	87.3	86.8
Deunsol Yoon et al. '18	2400D Multiple-Dynamic Self-Attention Model	7.0m	89.0	87.4

Other neural network models

Rocktäschel et al. '15	100D LSTMs w/ word-by-word attention	250k	85.3	83.5
Pengfei Liu et al. '16a	100D DF-LSTM	320k	85.2	84.6
Yang Liu et al. '16	600D (300+300) BiLSTM encoders with intra-attention and symbolic preproc.	2.8m	85.9	85.0
Pengfei Liu et al. '16b	50D stacked TC-LSTMs	190k	86.7	85.1
Munkhdalai & Yu '16a	300D MMA-NSE encoders with attention	3.2m	86.9	85.4
Wang & Jiang '15	300D mLSTM word-by-word attention model	1.9m	92.0	86.1
Jianpeng Cheng et al. '16	300D LSTMN with deep attention fusion	1.7m	87.3	85.7
Jianpeng Cheng et al. '16	450D LSTMN with deep attention fusion	3.4m	88.5	86.3
Parikh et al. '16	200D decomposable attention model	380k	89.5	86.3
Parikh et al. '16	200D decomposable attention model with intra-sentence attention	580k	90.5	86.8
Munkhdalai & Yu '16b	300D Full tree matching NTI-SLSTM-LSTM w/ global attention	3.2m	88.5	87.3
Zhiguo Wang et al. '17	BiMPM	1.6m	90.9	87.5
Lei Sha et al. '16	300D re-read LSTM	2.0m	90.7	87.5
Yichen Gong et al. '17	448D Densely Interactive Inference Network (DIIN, code)	4.4m	91.2	88.0
McCann et al. '17	Biattentive Classification Network + CoVe + Char	22m	88.5	88.1
Chuanqi Tan et al. '18	150D Multiway Attention Network	14m	94.5	88.3
Xiaodong Liu et al. '18	Stochastic Answer Network	3.5m	93.3	88.5
Ghaeini et al. '18	450D DR-BiLSTM	7.5m	94.1	88.5
Yi Tay et al. '18	300D CAFE	4.7m	89.8	88.5
Qian Chen et al. '17	KIM	4.3m	94.1	88.6
Qian Chen et al. '16	600D ESIM + 300D Syntactic TreeLSTM (code)	7.7m	93.5	88.6
Peters et al. '18	ESIM + ELMo	8.0m	91.6	88.7
Boyuan Pan et al. '18	300D DMAN	9.2m	95.4	88.8
Zhiguo Wang et al. '17	BiMPM Ensemble	6.4m	93.2	88.8
Yichen Gong et al. '17	448D Densely Interactive Inference Network (DIIN, code) Ensemble	17m	92.3	88.9
Seonhoon Kim et al. '18	Densely-Connected Recurrent and Co-Attentive Network	6.7m	93.1	88.9
Zhuosheng Zhang et al. '18	SLRC	6.1m	89.1	89.1
Qian Chen et al. '17	KIM Ensemble	43m	93.6	89.1
Ghaeini et al. '18	450D DR-BiLSTM Ensemble	45m	94.8	89.3
Peters et al. '18	ESIM + ELMo Ensemble	40m	92.1	89.3
Yi Tay et al. '18	300D CAFE Ensemble	17.5m	92.5	89.3
Chuanqi Tan et al. '18	150D Multiway Attention Network Ensemble	58m	95.5	89.4
Boyuan Pan et al. '18	300D DMAN Ensemble	79m	96.1	89.6
Radford et al. '18	Fine-Tuned LM-Pretrained Transformer	85m	96.6	89.9
Seonhoon Kim et al. '18	Densely-Connected Recurrent and Co-Attentive Network Ensemble	53.3m	95.0	90.1