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]: A recently published dataset called SCAN BIBREF2 (Simplified version of the CommAI Navigation tasks), tests compositional generalization in a sequence-to-sequence (seq2seq) setting by systematically holding out of the training set all inputs containing a basic primitive verb ("jump"), and testing on sequences containing that verb. 
 Question: How does the SCAN dataset evaluate compositional generalization?
[A]: it systematically holds out inputs in the training set containing basic primitive verb, "jump", and tests on sequences containing that verb.


[Q]: Because our approach is specifically intended to yield sentences that are grammatical, we additionally consider the following log ratio (i.e., the grammatical phrase over the ungrammatical phrase): 
 Question: How do they measure grammaticality?
[A]: by calculating log ratio of grammatical phrase over ungrammatical phrase


[Q]: We used four classification algorithms: 1) Logistic regression, which is conventionally used in sentiment classification. Other three algorithms which are relatively new and has shown great results on sentiment classification types of problems are: 2) Naïve Bayes with SVM (NBSVM), 3) Extreme Gradient Boosting (XGBoost) and 4) FastText algorithm with Bidirectional LSTM (FastText-BiLSTM). 
 Question: What state of the art models are used in the experiments?
[A]:
2) Naïve Bayes with SVM (NBSVM) 3) Extreme Gradient Boosting (XGBoost) 4) FastText algorithm with Bidirectional LSTM (FastText-BiLSTM)