End-to-End Multi-View Networks for Text Classification
Table 2: Ablation experiments on the Stanford Sentiment Treebank test set
['Full MVN', '51.5']
First, we construct a traditional ensemble model. We independently train eight MVN models, each with a single view, to serve as weak learners. We have them vote with equal weight for the final classification, obtaining a test-set accuracy of 50.2. Next, we restrict the views in the MVN to be unaware of each other. This drops performance to 49.0. This drops the test-set performance to 50.5. These experiments suggest that enabling the views to build upon each other is crucial for achieving the best performance.