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: As before, the training and test sets contain some 30,000 and 5,000 sentence pairs, respectively 
 Question: How many samples did they generate for the artificial language?

SOLUTION: 70,000

PROBLEM:  Recurrent Neural Network (RNN) Language Models
are trained to output the probability distribution of the upcoming word given a context, without explicitly representing the structure of the context BIBREF9, BIBREF10. ActionLSTM
models the linearized bracketed tree structure of a sentence by learning to predict the next action required to construct a phrase-structure parse BIBREF16. Generative Recurrent Neural Network Grammars (RNNG)
jointly model the word sequence as well as the underlying syntactic structure BIBREF18. 
 Question: What are the baseline models?

SOLUTION: Recurrent Neural Network (RNN) ActionLSTM Generative Recurrent Neural Network Grammars (RNNG)

PROBLEM: A quantitative measure is required to reliably evaluate the achieved improvement. One of the methods proposed to measure the interpretability is the word intrusion test BIBREF41 . But, this method is expensive to apply since it requires evaluations from multiple human evaluators for each embedding dimension. In this study, we use a semantic category-based approach based on the method and category dataset (SEMCAT) introduced in BIBREF27 to quantify interpretability. Specifically, we apply a modified version of the approach presented in BIBREF40 in order to consider possible sub-groupings within the categories in SEMCAT.  
 Question: What experiments do they use to quantify the extent of interpretability?

SOLUTION:
Human evaluation for interpretability using the word intrusion test and automated evaluation for interpretability using a semantic category-based approach based on the method and category dataset (SEMCAT).