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.

Example Input:  In terms of this, rather than starting from Twitter API Search, we look into the archived snapshots of two major news websites (CNN, NBC), and then extract the tweet blocks that are embedded in the news articles.
Example Output: How do they determine if tweets have been used by journalists?

Example Input: We focus primarily on the smaller data set of 645 hand-labeled articles provided to task participants, both for training and for validation.  We pre-trained BERT-base on the 600,000 articles without labels by using the same Cloze task BIBREF5 that BERT had originally used for pre-training. 
Example Output: How long is the dataset?

Example Input: Results are shown in Table TABREF11 . Results show that incorporating self-attention mechanism in the encoder is beneficial for most tasks. However, original models were better in some tasks (CR, MPQA, MRPC), suggesting that self-attention mechanism could sometimes introduce noise in sentence features. Overall, utilizing self-attentive sentence representation further improves performances in 5 out of 8 tasks.
Example Output:
How much better performing is the proposed method over the baselines?