Multi-Sentence Argument Linking
Table 13: P(recision), R(ecall), and F1 on event-based slot filling (GVDB) using ELMo at the sentence level. On average, the performance is outperformed by BERT.
['Field', 'Field', 'Strict Baseline', 'Strict Baseline', 'Strict Baseline', 'Strict Us', 'Strict Us', 'Strict Us', 'Partial Baseline', 'Partial Baseline', 'Partial Baseline', 'Partial Us', 'Partial Us', 'Partial Us']
With the same hyperparameters and feature choices, we perform an identical evaluation using ELMo instead of BERT. As the original documents are not tokenized, we use SpaCy 2.1.4 for finding sentence boundaries and tokenization. (weapon type, number of shots fired). While Pavlick et al. only make predictions for Victim. Name, Shooter. Name, Location.(City|Location), Time.(Time|Clock), and Weapon. Weapon, we perform predictions over all annotated span-based fields. BERT generally improves over ELMo across the board, but not by a sizeable margin. Despite the inability to directly compare, we nonetheless present a stronger and more comprehensive baseline for future work with GVDB.