Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks
Table 3: Ablation study results (%). Accuracy and macro-F1 scores are the average value over 3 runs with random initialization.
['Model', 'Twitter Acc.', 'Twitter F1', 'Lap14 Acc.', 'Lap14 F1', 'Rest14 Acc.', 'Rest14 F1', 'Rest15 Acc.', 'Rest15 F1', 'Rest16 Acc.', 'Rest16 F1']
To further examine the level of benefit that each component of ASGCN brings to the performance, an ablation study is performed on ASGCN-DG. We also present the results of BiLSTM+Attn as a baseline, which uses two LSTMs for the aspect and the context respectively.