Review Helpfulness Prediction with Embedding-Gated CNN
Table 2: Comparison with linguistic feature baselines and CNN models.
['[EMPTY]', 'Watches', 'Phone', 'Outdoor', 'Home', 'Elec.']
First, all the CNN based models consistently outperform non-CNN models, indicating their expressiveness over handcrafted features. Second, CNNc outperforms CNN when data is relatively insufficient (e.g., the domains “Watches” and “Phones”) and loses its edge on domains of abundant data (e.g., the domain “Electronics”). This is because when data size is smaller, the out-of-vocabulary problem (OOV) is more severe, and character-based representation is more beneficial. Third, CNNca consistently outperforms CNNc, showing that adding topic-based representations can further help the task. Last but not least, our proposed EG-CNN outperforms all CNN variants, which justifies the necessity of adding embedding gates. This further supports the importance of considering embedding gates. In all cases, EG-CNN significantly outperforms the baselines and yields better results than all CNN variants.