Transformation Networks for Target-Oriented Sentiment Classification The work was done when Xin Li was an intern at Tencent AI Lab. This project is substantially supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14203414).
Table 2: Settings of hyper-parameters.
['[BOLD] Hyper-params', '[BOLD] TNet-LF', '[BOLD] TNet-LF', '[BOLD] TNet-LF', '[BOLD] TNet-AS', '[BOLD] TNet-AS', '[BOLD] TNet-AS']
Specifically, all hyper-parameters are tuned on 20% randomly held-out training data and the hyper-parameter collection producing the highest accuracy score is used for testing. Our model has comparable number of parameters compared to traditional LSTM-based models as we reuse parameters in the transformation layers and BiLSTM.