Multi-Sentence Argument Linking
Table 9: Hyperparameters of the model trained on RAMS. Sizes of learned weights that are omitted from the table can be determined from these hyperparameters. As the argument spans are given to the model in our experiments, we skip the first pass of pruning. We do not clip gradients.
['Hyperparameter Embeddings', 'Hyperparameter role size', 'Value 50']
For our model, we learn a linear combination of the top layers (9, 10, 11, 12) of BERT-base cased, while we use the middle layers (6, 7, 8, 9) for the 6–9 ablation. For ELMo, we use all three layers and encode each sentence separately. We apply a lexical dropout of 0.5 to these embeddings. For the condition with no given argument spans, we consider all intrasentential spans up to 5 tokens in length. We include the score of each candidate argument span when pruning to encourage the model to keep correct spans.