Question: What are the effects of diverse versions of pertained word embeddings? 
Evidence: In the block “versions”, we see that each embedding version is crucial for good performance: performance drops in every single case. Though it is not easy to compare fairly different embedding versions in NLP tasks, especially when those embeddings were trained on different corpora of different sizes using different algorithms, our results are potentially instructive for researchers making decision on which embeddings to use for their own tasks.
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.
In the block “versions”, we see that each embedding version is crucial for good performance: performance drops in every single case. 
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: The answers are shown as follows:
* each embedding version is crucial for good performance