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: Results show that the CJFA encoder obtains significantly better phone classification accuracy than the VAE baseline and also than the CJFS encoder. These results are replicated for speaker recognition tasks. 
 Question: Which approach out of two proposed in the paper performed better in experiments?

SOLUTION: CJFA encoder 

PROBLEM: For comparison purpose, we build two baseline systems for each direction: one is use the traditional phrase-based statistical machine translation (SMT), the other one is the NMT system.  
 Question: what was the baseline?

SOLUTION: traditional phrase-based statistical machine translation (SMT) NMT system

PROBLEM: In this study, we concentrate our effort on re-assessing keyphrase extraction performance on three increasingly sophisticated levels of document preprocessing described below. 
 Question: what keyphrase extraction models were reassessed?

SOLUTION:
Answer with content missing: (LVL1, LVL2, LVL3) 
- Stanford CoreNLP
- Optical Character Recognition (OCR) system, ParsCIT 
- further abridge the input text from level 2 preprocessed documents to the following: title, headers, abstract, introduction, related work, background and conclusion.