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.

Example Input: 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?
Example Output: gating function Dynamic Memory

Example Input: We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps.
Question: How many tweats does MMHS150k contains, 150000?
Example Output: $150,000$ tweets

Example Input: These powerful ideas from the NLP community have been adapted by researchers from other domains to tasks beyond representation of words, including relational entities BIBREF1 , BIBREF2 , general text-based attributes BIBREF3 , descriptive text of images BIBREF4 , nodes in graph structure of networks BIBREF5 , and queries BIBREF6 , to name a few.
Question: What domains are considered that have such large vocabularies?
Example Output:
relational entities general text-based attributes descriptive text of images nodes in graph structure of networks queries