Multi-Sentence Argument Linking
Table 11: Hyperparameters of the model trained on GVDB.
['Hyperparameter Embeddings', 'Hyperparameter role size', 'Value 50']
We experiment with the feature-based version of BERT-base and with ELMo as our contextualized encoder. Since there is only one event per document and no explicit trigger, e is represented by a span embedding of the full document. We use the top four layers (9–12) of BERT-base cased (all three layers for ELMo) with a lexical dropout of 0.5. Everywhere else, we apply a dropout of 0.4. We train with the Adam optimizer Kingma and Ba Our best checkpoint was after 8 epochs (roughly 9 hours on a single NVIDIA 1080Ti). Even though the official evaluation is string based, we used a span-based micro F1 metric for early stopping.