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 present here a step in this direction: a probabilistic semantic parser that uses a large knowledge base (NELL) to form a prior probability distribution on the meanings of sentences it parses, and that "understands" each sentence either by identifying its existing beliefs that correspond to the sentence's meaning, or by creating new beliefs.

Output: What knowledge bases do they use?


Input: Consider Input: We quantify the efficiency-accuracy tradeoff compared to two rule-based baselines: Unif and Stopword. 


Output: What are the baselines used?