Long Short-Term Memory-Networks for Machine Reading
Table 2: Model accuracy (%) on the Sentiment Treebank (test set). The memory size of LSTMN models is set to 168 to be compatible with previously published LSTM variants (Tai et al., 2015).
['Models', 'Fine-grained', 'Binary']
Our second task concerns the prediction of sentiment labels of sentences. Following previous work on this dataset, we used 8,544 sentences for training, 1,101 for validation, and 2,210 for testing. The average sentence length is 19.1. In addition, we also performed a binary classification task (positive, negative) after removing the neutral label. This resulted in 6,920 sentences for training, 872 for validation and 1,821 for testing. We compared our model with a wide range of top-performing systems. Recursive models assume the input sentences are represented as parse trees and can take advantage of annotations at the phrase level. The number of layers for our models was set to be comparable to previously published results. Interestingly, the network learns to associate sentiment important words such as though and fantastic or not and good.