AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification
Table 4: Experiment results of our AC-BLSTM and G-AC-BLSTM model compared with other methods. Performance is measured in accuracy. CNN-non-static, CNN-multichannel: Convolutional neural network with fine-tuned word vectors and multi-channels (Kim, 2014). C-LSTM: Combining CNN and LSTM to model sentences (Zhou et al., 2015). Molding-CNN: A feature mapping operation based on tensor products on stacked vectors (Lei et al., 2015). UPNN(no UP): User product neural network without using user and product information (Tang et al., 2015). DSCNN, DSCNN-Pretrain: Dependency sensitive convolutional neural networks and with pretraind sequence autoencoders (Zhang et al., 2016a). MG-CNN(w2v+Syn+Glv), MGNC-CNN(w2v+Glv), MGNC-CNN(w2v+Syn+Glv): Multi-group norm constraint CNN with w2v:word2vec, Glv:GloVe (Pennington et al., 2014) and Syn: Syntactic embedding (Zhang et al., 2016b). NSC+LA: Neural sentiment classification model with local semantic attention (Chen et al., 2016a). SequenceModel(no UP): A sequence modeling-based neural network without using user and product information (Chen et al., 2016b). TreeBiGRU: A tree-structured attention recursive neural networks that incorporates a bidirectional approach with gated memory units for sentence classification (Kokkinos and Potamianos, 2017). TopCNNword, TopCNNsen, TopCNNword&sen, TopCNNens: Topic-aware convolutional neural network for sentence classification. TopCNNword means to use the word-topic probability information to enrich the word embeddings. TopCNNsen means to use the sentence-topic probability information to enrich the representation output of the pooling layer. TopCNNword&sen means to use both word-topic and sentence-topic probability information. TopCNNens means an ensemble model of the three variants of TopCNN models by averaging the class probability scores generated by the three models together (Zhao and Mao, 2017).
['[BOLD] Model', 'TREC', 'MR', 'SST-1', 'SST-2', 'SUBJ', 'YELP13']
We used standard train/test splits for those datasets that had them. Otherwise, we performed 10-fold cross validation. We repeated each experiment 10 times and report the mean accuracy. To the best of our knowledge, AC-BLSTM achieves the best results on five tasks.