TASK DEFINITION: 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.
PROBLEM: 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?

SOLUTION: gating function Dynamic Memory

PROBLEM: Using a set of semantically oriented tasks that require explicit semantic cross-lingual representations, we showed that mBERT contextual embeddings do not represent similar semantic phenomena similarly and therefore they are not directly usable for zero-shot cross-lingual tasks. 
 Question: What challenges this work presents that must be solved to build better language-neutral representations?

SOLUTION: contextual embeddings do not represent similar semantic phenomena similarly and therefore they are not directly usable for zero-shot cross-lingual tasks

PROBLEM: Our method is purely text-based, and ignores the publication date and the source of the article. It combines task-specific embeddings, produced by a two-level attention-based deep neural network model, with manually crafted features (stylometric, lexical, grammatical, and semantic), into a kernel-based SVM classifier. 
 Question: what types of features were used?

SOLUTION:
stylometric, lexical, grammatical, and semantic