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]: In this section, we introduce an invertibility condition for our neural projector to tackle the optimization challenge. Specifically, we constrain our neural projector with two requirements: (1) INLINEFORM0 and (2) INLINEFORM1 exists.  
 Question: What is the invertibility condition?
[EX A]: The neural projector must be invertible.

[EX Q]: This working note presents RNN and LSTM based embedding system for social media health text classification. Though the data sets of task 1 and task 2 are limited, this paper proposes RNN and LSTM based embedding method. 
 Question: What type of RNN is used?
[EX A]:
RNN LSTM