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.

[Q]: We collect three years of online news articles from June 2016 to June 2019.
Question: What unlabeled corpus did they use?
[A]: three years of online news articles from June 2016 to June 2019

[Q]: This model uses a particular RNN cell in order to store just relevant information about the given question. Dynamic Memory stores information of entities present in $T$ . The addition of the $s_t^T q$ term in the gating function is our main contribution.
Question: How does the model recognize entities and their relation to answers at inference time when answers are not accessible?
[A]: gating function Dynamic Memory

[Q]: We published a survey on Reddit asking Danish speaking users to suggest offensive, sexist, and racist terms for a lexicon. Language and user behaviour varies between platforms, so the goal is to capture platform-specific terms. This gave 113 offensive and hateful terms which were used to find offensive comments. The remainder of comments in the corpus were shuffled and a subset of this corpus was then used to fill the remainder of the final dataset. The resulting dataset contains 3600 user-generated comments, 800 from Ekstra Bladet on Facebook, 1400 from r/DANMAG and 1400 from r/Denmark.
Question: How large was the dataset of Danish comments?
[A]:
3600 user-generated comments