End-to-End Multi-View Networks for Text Classification

By Hongyu Guo, Colin Cherry, Jiang Su. 2017

12 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

Citations in the corpus

• Learned in Translation: Contextualized Word Vectors (2017, neutral): “MVN [Guo et al., 2017] 51.5 T R E C - 50 SVM [Loni et al., 2011] 89.0 DMN [Kumar et al., 2016] 52.1 SNoW [Li and Roth, 2006] 89.3 LSTM-CNN [Zhou et al., 2016] 52.4 BCN+Char+CoVe (Ours) 90.2 TE-LSTM [Huang et al., 2017] 52.6 RulesUHC [da Silva et al., 2011] 90.8 NTI [Munkhdalai and Yu, 2016b] 53…”