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]: The split methods (beyond random split) are the following:

Output length: Variation of the setup described by BIBREF2 where the train set consists of examples with output (sparql query or action sequence) length $\le \hspace{-2.5pt} N$, while the test set consists of examples with output length $> \hspace{-2.5pt} N$. For CFQ, we use $N = 7$ constraints. For scan, we use $N = 22$ actions.

Input length: Variation of the above setup, in which the train set consists of examples with input (question or command) length $\le N$, while test set consists of examples with input length $> N$. For CFQ, we use $N=19$ grammar leaves. For SCAN, we use $N=8$ tokens.

Output pattern: Variation of setup described by BIBREF8, in which the split is based on randomly assigning clusters of examples sharing the same output (query or action sequence) pattern. Query patterns are determined by anonymizing entities and properties; action sequence patterns collapse primitive actions and directions.

Input pattern: Variation of the previous setup in which the split is based on randomly assigning clusters of examples sharing the same input (question or command) pattern. Question patterns are determined by anonymizing entity and property names ; command patterns collapse verbs and the interchangeable pairs left/right, around/opposite, twice/thrice. 
 Question: What are other approaches into creating compositional generalization benchmarks?
[EX A]: random  Output length Input length Output pattern Input pattern

[EX Q]: A few sample advice rules in English (these are converted to first-order logic format and given as input to our algorithm) are presented in Table TABREF11 .  We modified the work of Odom et al. odomAIME15,odomAAAI15 to learn RDNs in the presence of advice. The key idea is to explicitly represent advice in calculating gradients. 
 Question: How do they incorporate human advice?
[EX A]: by converting human advice to first-order logic format and use as an input to calculate gradient

[EX Q]: We assessed the proposed models on four different NLG domains: finding a restaurant, finding a hotel, buying a laptop, and buying a television. The Restaurant and Hotel were collected in BIBREF4 , while the Laptop and TV datasets have been released by BIBREF22 with a much larger input space but only one training example for each DA so that the system must learn partial realization of concepts and be able to recombine and apply them to unseen DAs.  
 Question: Does the model evaluated on NLG datasets or dialog datasets?
[EX A]:
NLG datasets