Deep Structured Neural Network for Event Temporal Relation Extraction
Table 8: Ablation study on linguistic feature usage. Additional linguistic features do not lead to significant improvement and even hurt performance in 2 out of 3 datasets. The results show that our proposed framework is semantic-rich and capable of avoiding the usage of additional linguistic feature.
['[EMPTY]', 'local- w/ feat.', 'global- w/ feat.', 'local- w/o feat.', 'global- w/o feat.']
Previous research establish the success of leveraging linguistic features in event relation prediction. One advantage of leveraging contextualized word embedding is to provide rich semantic representation and could potentially avoid the usage of extra linguistic features. Here, we study the impact of incorporating linguistic features to our model by using simple features provided in the original datasets: token distance, tense and polarity of event entities. These additional features likely cause over-fitting and hence do not improve model performance across all three datasets we test. This set of experiments show that linguistic features do not improve the predicting power of our current framework.