In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

In order to understand the latent topics of those #MeToo tweets for college followers, we first utilize Latent Dirichlet Allocation (LDA) to label universal topics demonstrated by the users. Since certain words frequently appear in those #MeToo tweets (e.g., sexual harassment, men, women, story, etc.), we transform our corpus using TF-IDF, a term-weighting scheme that discounts the influence of common terms. 
 Question: How are the topics embedded in the #MeToo tweets extracted?
Abstractive

These models define parametrized similarity scoring functions $: Q\times T\rightarrow \mathbb {R}$ , where $Q$ is the set of natural language questions and $T$ is the set of paraphrases of logical forms. 
 Question: Does a neural scoring function take both the question and the logical form as inputs?
Yes-no

We choose the state-of-the-art Transformer BIBREF1 model and the conventional RNN-Search model BIBREF0 as our test bed. 
 Question: Which model architectures do they test their word importance approach on?

Extractive

