In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Q: Category II and III errors are harder to avoid and could be improved by applying reasoning BIBREF36 or irony detection methods BIBREF37.

A: What recommendations are made to improve the performance in future?
****
Q: The resulting dataset consists of 22,880 users, 41,094 blogs, and 561,003 posts. Table TABREF2 presents additional statistics of our dataset.

A: How many users do they look at?
****
Q: We use the additional INLINEFORM0 training articles labeled by publisher as an unsupervised data set to further train the BERT model. We first investigate the impact of pre-training on BERT-BASE's performance.  On the same computer, fine tuning the model on the small training set took only about 35 minutes for sequence length 100. 

A:
How are the two different models trained?
****