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.
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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: Figure FIGREF23 shows the proposed ontology, which, in our evaluation procedure, was populated with 3121 events entries from 51 documents. 
 Question: How is the effectiveness of this pipeline approach evaluated?

Answer: proposed ontology, which, in our evaluation procedure, was populated with 3121 events entries from 51 documents.


Question: The semi-parametric models were trained on 32 GPUs with each replica split over 2 GPUs, one to train the translation model and the other for computing the CSTM. 
 Question: Does their combination of a non-parametric retrieval and neural network get trained end-to-end?

Answer:
Yes