Question: How much gain does the model achieve with pretraining MVCNN?
Evidence: FLOAT SELECTED: Table 3: Test set results of our CNN model against other methods. RAE: Recursive Autoencoders with pretrained word embeddings from Wikipedia (Socher et al., 2011b). MV-RNN: Matrix-Vector Recursive Neural Network with parse trees (Socher et al., 2012). RNTN: Recursive Neural Tensor Network with tensor-based feature function and parse trees (Socher et al., 2013). DCNN, MAX-TDNN, NBOW: Dynamic Convolution Neural Network with k-max pooling, Time-Delay Neural Networks with Max-pooling (Collobert and Weston, 2008), Neural Bag-of-Words Models (Kalchbrenner et al., 2014). Paragraph-Vec: Logistic regression on top of paragraph vectors (Le and Mikolov, 2014). SVM, BINB, MAXENT: Support Vector Machines, Naive Bayes with unigram features and bigram features, Maximum Entropy (Go et al., 2009). NBSVM, MNB: Naive Bayes SVM and Multinomial Naive Bayes with uni-bigrams from Wang and Manning (2012). CNN-rand/static/multichannel/nonstatic: CNN with word embeddings randomly initialized / initialized by pretrained vectors and kept static during training / initialized with two copies (each is a “channel”) of pretrained embeddings / initialized with pretrained embeddings while fine-tuned during training (Kim, 2014). G-Dropout, F-Dropout: Gaussian Dropout and Fast Dropout from Wang and Manning (2013). Minus sign “-” in MVCNN (-Huang) etc. means “Huang” is not used. “versions / filters / tricks / layers” denote the MVCNN variants with different setups: discard certain embedding version / discard certain filter size / discard mutual-learning or pretraining / different numbers of convolution layer.
FLOAT SELECTED: Table 3: Test set results of our CNN model against other methods. RAE: Recursive Autoencoders with pretrained word embeddings from Wikipedia (Socher et al., 2011b). MV-RNN: Matrix-Vector Recursive Neural Network with parse trees (Socher et al., 2012). RNTN: Recursive Neural Tensor Network with tensor-based feature function and parse trees (Socher et al., 2013). DCNN, MAX-TDNN, NBOW: Dynamic Convolution Neural Network with k-max pooling, Time-Delay Neural Networks with Max-pooling (Collobert and Weston, 2008), Neural Bag-of-Words Models (Kalchbrenner et al., 2014). Paragraph-Vec: Logistic regression on top of paragraph vectors (Le and Mikolov, 2014). SVM, BINB, MAXENT: Support Vector Machines, Naive Bayes with unigram features and bigram features, Maximum Entropy (Go et al., 2009). NBSVM, MNB: Naive Bayes SVM and Multinomial Naive Bayes with uni-bigrams from Wang and Manning (2012). CNN-rand/static/multichannel/nonstatic: CNN with word embeddings randomly initialized / initialized by pretrained vectors and kept static during training / initialized with two copies (each is a “channel”) of pretrained embeddings / initialized with pretrained embeddings while fine-tuned during training (Kim, 2014). G-Dropout, F-Dropout: Gaussian Dropout and Fast Dropout from Wang and Manning (2013). Minus sign “-” in MVCNN (-Huang) etc. means “Huang” is not used. “versions / filters / tricks / layers” denote the MVCNN variants with different setups: discard certain embedding version / discard certain filter size / discard mutual-learning or pretraining / different numbers of convolution layer.
Answer: 0.8 points on Binary; 0.7 points on Fine-Grained; 0.6 points on Senti140; 0.7 points on Subj