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.
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Question: Since the ground-truth reference captions in ShapeWorld are randomly sampled, we take the sampled captions accompanying the test images as a proxy for optimal caption diversity, and compare it with the empirical output diversity of the evaluated model on these test images. Practically, we look at language constructions used and compute the corresponding diversity score as the ratio of observed number versus optimal number

Answer: How is diversity measured?


Question: Our corpus comprises a total of 6,127 scientific entities, including 2,112 Process, 258 Method, 2,099 Material, and 1,658 Data entities. 

Answer: How large is the dataset?


Question: Note that ESIM models have two LSTM layers, the first (input) LSTM performs the input encoding and the second (inference) LSTM generates the representation for inference.

Answer:
How many layers are there in their model?