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.

[EX Q]: Since the training data consists only of utterance-denotation pairs, the ranker is trained to maximize the log-likelihood of the correct answer $z$ by treating logical forms as a latent variable The role of the ranker is to score the candidate logical forms generated by the parser; at test time, the logical form receiving the highest score will be used for execution. The ranker is a discriminative log-linear model over logical form $y$ given utterance $x$ :  
 Question: How does the model compute the likelihood of executing to the correction semantic denotation?
[EX A]: By treating logical forms as a latent variable and training a discriminative log-linear model over logical form y given x.

[EX Q]: We evaluate the generations with the ROUGE BIBREF29 and METEOR BIBREF30 metrics using the true sentences as targets. 
 Question: What metrics are used for evaluation?
[EX A]: ROUGE BIBREF29 and METEOR BIBREF30

[EX Q]: Amazon Reviews Dataset BIBREF24 is a large dataset with millions of reviews from different product categories. For our experiments, we consider a subset of 20000 reviews from the domains Cell Phones and Accessories(C), Clothing and Shoes(S), Home and Kitchen(H) and Tools and Home Improvement(T). Out of 20000 reviews, 10000 are positive and 10000 are negative. We use 12800 reviews for training, 3200 reviews for validation and 4000 reviews for testing from each domain. 
 Question: For the purposes of this paper, how is something determined to be domain specific knowledge?
[EX A]:
reviews under distinct product categories are considered specific domain knowledge