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]: The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module. We use a triplet network BIBREF41 , BIBREF42 in our representation module. The triplet network consists of three sub-parts: target, supporting, and contrasting networks. All three networks share the same parameters. The Mixture module brings the image and caption embeddings to a joint feature embedding space. 
 Question: How do the authors define a differential network?
[A]: The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module.


[Q]: We draw on a recently released corpus of state speeches delivered during the annual UN General Debate that provides the first dataset of textual output from states that is recorded at regular time-series intervals and includes a sample of all countries that deliver speeches BIBREF11 .  
 Question: Which dataset do they use?
[A]: corpus of state speeches delivered during the annual UN General Debate


[Q]: Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'. 
 Question: How do they extract causality from text?
[A]:
They identify documents that contain the unigrams 'caused', 'causing', or 'causes'