In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Input: Consider Input: The dataset includes about 6,000 triples, comprised of videos, questions, and answer spans manually collected from screencast tutorial videos with spoken narratives for a photo-editing software.
Question: What kind of instructional videos are in the dataset?

Output: tutorial videos for a photo-editing software

Input: Consider Input: Machine-machine Interaction A related line of work explores simulation-based dialogue generation, where the user and system roles are simulated to generate a complete conversation flow, which can then be converted to natural language using crowd workers BIBREF1. Such a framework may be cost-effective and error-resistant since the underlying crowd worker task is simpler, and semantic annotations are obtained automatically. It is often argued that simulation-based data collection does not yield natural dialogues or sufficient coverage, when compared to other approaches such as Wizard-of-Oz. We argue that simulation-based collection is a better alternative for collecting datasets like this owing to the factors below.
Question: How did they gather the data?

Output: simulation-based dialogue generation, where the user and system roles are simulated to generate a complete conversation flow, which can then be converted to natural language using crowd workers

Input: Consider Input: We collected a total of 657 ratings by 14 volunteers, 5 Italian and 9 non-Italian listeners, spread over the 24 clips and three testing conditions.
Question: How many people are employed for the subjective evaluation?
Output: 14 volunteers