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.

Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings. 
 Question: How big is improvement over the old  state-of-the-art performance on CoNLL-2009 dataset?
our Open model achieves more than 3 points of f1-score than the state-of-the-art result

We compare our sentence classification with the LSTM baseline and evaluate the biggest set of PICO sentence data available at this point BIBREF13. SCIBERT was chosen as an additional baseline model for fine-tuning because it provided the best representation of embedded PICO sentences.  
 Question: What baselines did they consider?
LSTM SCIBERT

Dataset: Based on some earlier work, only available labelled dataset had 3189 rows of text messages of average length of 116 words and with a range of 1, 1295. 
 Question: How big is the dataset?

3189 rows of text messages