k-Nearest Neighbor Augmented Neural Networks for Text Classification
Table 3: Evaluation on the test sets, and the state-of-the-art models by Zhang, Zhao, and LeCun (2015)a, Xiao and Cho (2016)b, Conneau et al. (2016)c, and Joulin et al. (2016)d.
['Model', 'AG', 'Sogou', 'DBP', 'Yelp P.', 'Yelp F.', 'Yah. A', 'Amz. F.', 'Amz. P.']
We construct two models to evaluate on all of the test sets. We find that by utilizing the kNN memory, our BiLSTM with kNN model outperforms the baseline on all datasets. Our model even beats the VDCNN model on the AG’s News, DBpeida and Yahoo! Answers datasets, which shows the effectiveness of our method. Moreover, our kNN memory can be easily adapted into these more complex neural network models.