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.

[EX Q]: We perform our experiments using ActivityNet Captions dataset BIBREF2 that is considered as the standard benchmark for dense video captioning task. The dataset contains approximately 20k videos from YouTube and split into 50/25/25 % parts for training, validation, and testing, respectively.
[EX A]: What domain does the dataset fall into?

[EX Q]: CFQ contains 239,357 English question-answer pairs that are answerable using the public Freebase data.
[EX A]: How big is new question answering dataset?

[EX Q]: Specifically, we group the models based on the objective function it optimizes. We believe this work can aid the understanding of the existing literature. We believe that the performance of these models is highly dependent on the objective function it optimizes predicting adjacent word (within-tweet relationships), adjacent tweet (inter-tweet relationships), the tweet itself (autoencoder), modeling from structured resources like paraphrase databases and weak supervision.
[EX A]:
How do they encourage understanding of literature as part of their objective function?