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: The main contribution of our work is thus a new parallel data-to-text NLG corpus that (1) is more conversational, rather than information seeking or question answering, and thus more suitable for an open-domain dialogue system, (2) represents a new, unexplored domain which, however, has excellent potential for application in conversational agents, and (3) has high-quality, manually cleaned human-produced utterances.

Output: How the authors made sure that corpus is clean despite being crowdsourced?

Input: Consider Input: The BLEU metric is adopted to evaluate the model performance during evaluation.

Output: What evaluation metric is used?

Input: Consider Input: DeepMine is publicly available for everybody with a variety of licenses for different users. It was collected using crowdsourcing BIBREF4. The data collection was done using an Android application. Each respondent installed the application on his/her personal device and recorded several phrases in different sessions.
Output: how was the speech collected?