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.

Input: Consider Input: We design the word-level refine decoder because this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences.

Output: Why masking words in the decoder is helpful?


Input: Consider Input:  We use the $\textsc {BERT}_{\textsc {BASE}}$ ensemble from BIBREF3 as the single-hop QA model. 

Output: What off-the-shelf QA model was used to answer sub-questions?


Input: Consider Input: The output of a system with the target words in the predicted order is compared to the gold ranking of the DURel data set. As the metric to assess how well the model's output fits the gold ranking Spearman's $\rho $ was used. The higher Spearman's rank-order correlation the better the system's performance.


Output: How is evaluation performed?