Multi-Sentence Argument Linking
Table 12: P(recision), R(ecall), and F1 on event-based slot filling (GVDB) using BERT as the document encoder. Due to the different data splits and evaluation conditions, the results are not directly comparable to the baseline Pavlick et al. (2016), which is provided only for reference. Fields that were aggregated in the baseline are predicted separately in our model. ‘–’ indicates result is not reported in the baseline.
['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']
The gun violence database: a new task and data set for NLP. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, pp. 1018–1024. External Links: Link, Document Cited by: 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.