instruction:
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.
question:
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?
answer:
The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module.


question:
This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-related queries (Table TABREF6 ) within a specific time frame.  
 Question: Do they evaluate only on English data?
answer:
Yes


question:
We used recordings of nurse-initiated telephone conversations for congestive heart failure patients undergoing telemonitoring, post-discharge from the hospital. To analyze the linguistic structure of the inquiry-response pairs in the entire 41-hour dataset, we randomly sampled a seed dataset consisting of 1,200 turns and manually categorized them to different types, which are summarized in Table TABREF14 along with the corresponding occurrence frequency statistics. 
 Question: Which data do they use as a starting point for the dialogue dataset?
answer:

A sample from nurse-initiated telephone conversations for congestive heart failure patients undergoing telepmonitoring, post-discharge from the Health Management Unit at Changi General Hospital