Neural Network Models for Implicit Discourse Relation Classification in English and Chinese without Surface Features
Table 2: Performance comparison across different models for second-level senses.
['Model', 'Accuracy']
k is the word vector size. The feedforward model performs best overall among all of the neural architectures we explore Tree LSTM achieves inferior accuracy than our best feedforward model. The best configuration of the feedforward model uses 300-dimensional word vectors, one hidden layer, and the summation pooling function to derive argument feature vectors. The model behaves well during training and converges in less than an hour on a CPU.