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 opted for an automatic approach instead, that can be scaled arbitrarily and at little cost: we generate synthetic sentence pairs $(, \tilde{})$ by randomly perturbing 1.8 million segments $$ from Wikipedia. We use three techniques: mask-filling with BERT, backtranslation, and randomly dropping out words.  
 Question: How are the synthetic examples generated?
answer:
Random perturbation of Wikipedia sentences using mask-filling with BERT, backtranslation and randomly drop out


question:
For English dataset, we crawl 687 cybersecurity articles from a collection of advanced persistent threats (APT) reports which are published from 2008 to 2018.  
 Question: Where are the cybersecurity articles used in the model sourced from?
answer:
 from a collection of advanced persistent threats (APT) reports which are published from 2008 to 2018


question:
The model is composed of an encoder (§SECREF5) and a decoder with the attention mechanism (§SECREF7) that are both implemented using recurrent neural networks (RNNs); the encoder converts source words into a sequence of vectors, and the decoder generates target language words one-by-one with the attention mechanism based on the conditional probability shown in the equation DISPLAY_FORM2 and DISPLAY_FORM3. 
 Question: Which model architecture do they use to build a model?
answer:

model is composed of an encoder (§SECREF5) and a decoder with the attention mechanism (§SECREF7) that are both implemented using recurrent neural networks (RNNs)