AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification
This work addresses text classification problems for NLP researchers and practitioners, presenting an incremental improvement by hybridizing existing methods.
The authors tackled text classification tasks by proposing AC-BLSTM, a framework combining asymmetric convolutional neural networks with bidirectional LSTM, achieving state-of-the-art results on five tasks including sentiment analysis and question type classification.
Recently deeplearning models have been shown to be capable of making remarkable performance in sentences and documents classification tasks. In this work, we propose a novel framework called AC-BLSTM for modeling sentences and documents, which combines the asymmetric convolution neural network (ACNN) with the Bidirectional Long Short-Term Memory network (BLSTM). Experiment results demonstrate that our model achieves state-of-the-art results on five tasks, including sentiment analysis, question type classification, and subjectivity classification. In order to further improve the performance of AC-BLSTM, we propose a semi-supervised learning framework called G-AC-BLSTM for text classification by combining the generative model with AC-BLSTM.