We also present a set of strong, BERT-based neural baselines achieving an f1-score of 70. 0 for Claim and 62. 4 for Evidence identification evaluated with 10-fold cross-validation.
We study the effect of seven data augmentation (da) methods in factoid question answering, focusing on the biomedical domain, where obtaining training instances is particularly difficult.
To test our key findings on another dataset, we modified the Natural Questions dataset so that it can also be used for document and snippet retrieval.
Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better.
Many existing NE methods rely only on network structure, overlooking other information associated with the nodes, e. g., text describing the nodes.