Abstract: We propose a multi-view network for text classification. Our method automatically creates various views of its input text, each taking the form of soft attention weights that distribute the classifier's focus among a set of base features. For a bag-of-words representation, each view focuses on a different subset of the text's words. Aggregating many such views results in a more discriminative and robust representation. Through a novel architecture that both stacks and concatenates views, we produce a network that emphasizes both depth and width, allowing training to converge quickly. Using our multi-view architecture, we establish new state-of-the-art accuracies on two benchmark tasks.

Introduction: State-of-the-art deep neural networks leverage task-specific architectures to develop hierarchical representations of their input, with each layer building a refined abstraction of the layer that came before it BIBREF0 . For text classification, one can think of this as a single reader building up an increasingly refined understanding of the content. In a departure from this philosophy, we propose a divide-and-conquer approach, where a team of readers each focus on different aspects of the text, and then combine their representations to make a joint decision. More precisely, the proposed Multi-View Network (MVN) for text classification learns to generate several views of its input text. Each view is formed by focusing on different sets of words through a view-specific attention mechanism. These views are arranged sequentially, so each subsequent view can build upon or deviate from previous views as appropriate. The final representation that concatenates these diverse views should be more robust to noise than any one of its components. Furthermore, different sentences may look similar under one view but different under another, allowing the network to devote particular views to distinguishing between subtle differences in sentences, resulting in more discriminative representations. Unlike existing multi-view neural network approaches for image processing BIBREF1 , BIBREF2 , where multiple views are provided as part of the input, our MVN learns to automatically create views from its input text by focusing on different sets of words. Compared to deep Convolutional Networks (CNN) for text BIBREF3 , BIBREF0 , the MVN strategy emphasizes network width over depth. Shorter connections between each view and the loss function enable better gradient flow in the networks, which makes the system easier to train. Our use of multiple views is similar in spirit to the weak learners used in ensemble methods BIBREF4 , BIBREF5 , BIBREF6 , but our views produce vector-valued intermediate representations instead of classification scores, and all our views are trained jointly with feedback from the final classifier. Experiments on two benchmark data sets, the Stanford Sentiment Treebank BIBREF7 and the AG English news corpus BIBREF3 , show that 1) our method achieves very competitive accuracy, 2) some views distinguish themselves from others by better categorizing specific classes, and 3) when our base bag-of-words feature set is augmented with convolutional features, the method establishes a new state-of-the-art for both data sets.

Question: which benchmark tasks did they experiment on?
Answer:  They used Stanford Sentiment Treebank benchmark for sentiment classification task and   AG English news corpus for the text classification task.