TASK DEFINITION: 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.
PROBLEM: 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?

SOLUTION: 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)

PROBLEM: Furthermore, the basic seq2seq assumes a strict order between generated tokens, but in reality, we should not severely punish the model when it predicts the correct tokens in the wrong order. 
 Question: Do they impose any grammatical constraints over the generated output?

SOLUTION: No

PROBLEM: In phase 1 (also referred to as the “exploration” phase) the algorithm explores the state space through keeping track of previously visited states by maintaining an archive. During this phase, instead of resuming the exploration from scratch, the algorithm starts exploring from promising states in the archive to find high performing trajectories. 
 Question: How is trajectory with how rewards extracted?

SOLUTION:
explores the state space through keeping track of previously visited states by maintaining an archive