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 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?
[A]: by converting human advice to first-order logic format and use as an input to calculate gradient


[Q]: In its general form, a type-logical grammar consists of following components: 
 Question: Does Grail accept Prolog inputs?
[A]: No


[Q]: We use three encoder-decoder neural architectures as baselines: (1) LSTM+attention as an LSTM BIBREF19 with attention mechanism BIBREF20; (2) Transformer BIBREF21 and (3) Universal Transformer BIBREF22. 
 Question: What three machine architectures are analyzed?
[A]:
LSTM+attention Transformer  Universal Transformer