TASK DEFINITION: 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.
PROBLEM: We further explore the effect of INLINEFORM0 -gram length in our model (i.e., the filter size for the covolutional layers used by the attention parsing module). In Figure FIGREF39 we plot the AUC scores for link prediction on the Cora dataset against varying INLINEFORM1 -gram length.  
 Question: Do they measure how well they perform on longer sequences specifically?

SOLUTION: Yes-no

PROBLEM: Our system significantly out-performed the baseline on the joint task, as the baseline scored significantly lower for the gender task than for the variety task. 
 Question: On which task does do model do best?

SOLUTION: Abstractive

PROBLEM: We make copies of the monolingual model for each language and add additional crosslingual latent variables (CLVs) to couple the monolingual models, capturing crosslingual semantic role patterns. Concretely, when training on parallel sentences, whenever the head words of the arguments are aligned, we add a CLV as a parent of the two corresponding role variables.  
 Question: Which additional latent variables are used in the model?

SOLUTION:
Extractive