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:
Our training data consists of 2.09M sentence pairs extracted from LDC corpus. To test different approaches on Chinese-to-English translation task, we use NIST 2003(MT03) dataset as the validation set, and NIST 2004(MT04), NIST 2005(MT05), NIST 2006(MT06) datasets as our test sets. For English-to-Chinese translation task, we also use NIST 2003(MT03) dataset as the validation set, and NIST 2008(MT08) will be used as test set. 
 Question: What dataset did they use?
answer:
LDC corpus NIST 2003(MT03) NIST 2004(MT04), NIST 2005(MT05), NIST 2006(MT06) NIST 2008(MT08)


question:
We re-implement the model proposed in BIBREF3, and use it as a baseline for our problem. The rationale behind choosing this particular model as a baseline is it's proven good predictive performance on multilingual text classification. 
 Question: What is their baseline model?
answer:
the model proposed in BIBREF3


question:
We investigate both methods, either in isolation or combined, on two translation directions (En-It and En-De) for which the length of the target is on average longer than the length of the source. En-It, En-De in both directions 
 Question: Which languages do they focus on?
answer:

two translation directions (En-It and En-De)