In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

We also looked at where there was complete agreement by all annotators that a triple extraction was incorrect. In total there were 138 of these triples originating from 76 unique sentences.  
 Question: What is the most common error type?
all annotators that a triple extraction was incorrect

Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts. 
 Question: What data is the language model pretrained on?
Chinese general corpus

This results in three additional vectors corresponding to INLINEFORM3 , INLINEFORM4 and INLINEFORM5 difference vectors.

Results 
 Question: How are the EAU text spans annotated?

Answer with content missing: (Data and pre-processing section) The data is suited for our experiments because the annotators were explicitly asked to provide annotations on a clausal level.