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.

Q: In addition, we note that our approach can still lead to erroneous facts or even hallucinations. An interesting perspective might be to further constrain the model on the data structure in order to prevent inaccurate of even contradictory descriptions. 
 Question: What future possible improvements are listed?

A: rther constrain the model on the data structure in order to prevent inaccurate of even contradictory descriptions
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Q: Named Entity Recognition (NER) in the Biomedical domain usually includes recognition of entities such as proteins, genes, diseases, treatments, drugs, etc.  
 Question: What is NER?

A: Named Entity Recognition
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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?

A:
By treating logical forms as a latent variable and training a discriminative log-linear model over logical form y given x.
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