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: The test-set accuracies obtained by different learning methods, including the current state-of-the-art results, are presented in Table TABREF11 . 
 Question: what models did they compare to?

SOLUTION: High-order CNN, Tree-LSTM, DRNN, DCNN, CNN-MC, NBoW and SVM 

PROBLEM: We evaluated the proposed model using the publicly available LibriSpeech ASR corpus BIBREF23. The LibriSpeech dataset consists of 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset. 
 Question: How big is LibriSpeech dataset?

SOLUTION: 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset

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)