Building Context-aware Clause Representations for Situation Entity Type Classification
Table 3: Situation Entity Type Classification Results on the Test Set of MASC+Wiki. We report accuracy (Acc) and macro-average F1 (Macro).
['Model', 'Macro', 'Acc']
The second section shows the result of our implemented clause-level Bi-LSTM baseline, which already outperforms the previous best model. This result proves the effectiveness of the Bi-LSTM + max pooling approach in clause representation learning Conneau et al. The third section reports the performance of the paragraph-level models that uses paragraph-wide contexts as input. Compared with the baseline clause-level Bi-LSTM model, the basic paragraph-level model achieves 3.5% and 3.3% of performance gains in macro-average F1-score and accuracy respectively. Building on top of the basic paragraph-level model, the CRF layer further improves the SE type prediction performance slightly by 0.4% and 0.7% in macro-average F1-score and accuracy respectively. Therefore, our full model with the CRF layer achieves the state-of-the-art performance on the MASC+Wiki corpus.