Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding
Table 3: Ablation study on modules’ influence on the multi-granularity reader.
['[EMPTY]', 'Head Noun Match Precision', 'Head Noun Match Recall', 'Head Noun Match F-1', 'Exact Match Precision', 'Exact Match Recall', 'Exact Match F-1']
From the results, we find that: (1) when replacing the gated fusion operation with the simple sum of the sentence- and paragraph-level token representations, the precision and F-1 drop substantially, which proves the importance of dynamically incorporating context; (2) when removing the BERT’s contextualized representations, the model becomes more conservative and yields substantially lower recall and F-1; (3) when replacing the CRF layer and make independent labeling decisions for each token, both the precision and recall drops substantially.