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 library design of Torch-Struct follows the distributions API used by both TensorFlow and PyTorch BIBREF29. 
 Question: Does API provide ability to connect to models written in some other deep learning framework?
[EX A]: Yes

[EX Q]: In this paper we propose two neural embedding models in order to learn continuous concept vectors based on the skip-gram model BIBREF11 . Our first model is the Concept Raw Context model (CRC) which utilizes concept mentions in a large scale KB to jointly learn embeddings of both words and concepts. Our second model is the Concept-Concept Context model (3C) which learns the embeddings of concepts from their conceptual contexts (i.e., contexts containing surrounding concepts only).  
 Question: What are the two neural embedding models?
[EX A]: Our first model is the Concept Raw Context model (CRC) which utilizes concept mentions in a large scale KB to jointly learn embeddings of both words and concepts. Our second model is the Concept-Concept Context model (3C) which learns the embeddings of concepts from their conceptual contexts (i.e., contexts containing surrounding concepts only)

[EX Q]: In BIBREF8 a refined collection of tweets gathered from twitter is presented. Their dataset which is labeled for named entity recognition task contains 8,257 tweets. There are 12,784 entities in total in this dataset. Table TABREF19 shows statistics related to each named entity in training, development and test sets.

 
 Question: Which social media platform is explored?
[EX A]:
twitter 

