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:
We only used models of dimension 300 and, for Word2Vec, Wang2Vec, and FastText, skip-gram architectured models. 
 Question: what word embedding techniques did they experiment with?
answer:
Word2Vec, Wang2Vec, and FastText


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:
 As well as refining the annotation guidelines, the development process trained annotators who were not security experts. The development and test sets for Darkode and Hack Forums were annotated by additional team members (five for Darkode, one for Hack Forums), and then every disagreement was discussed and resolved to produce a final annotation. The authors, who are researchers in either NLP or computer security, did all of the annotation. 
 Question: Who annotated the data?
answer:

annotators who were not security experts researchers in either NLP or computer security