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.

Ex Input:
We create three additional training datasets by adding sentences involving object RCs to the original Wikipedia corpus (Section lm). To this end, we randomly pick up 30 million sentences from Wikipedia (not overlapped to any sentences in the original corpus), parse by the same parser, and filter sentences containing an object RC, amounting to 680,000 sentences.
Question: How do they perform data augmentation?

Ex Output:
They randomly sample sentences from Wikipedia that contains an object RC and add them to training data

Ex Input:
To evaluate the usefulness of our corpus for SMT purposes, we used it to train an automatic translator with Moses BIBREF8 .
Question: What SMT models did they look at?

Ex Output:
automatic translator with Moses

Ex Input:
For this reason as a baseline algorithm for English dataset we refer to results from BIBREF0, and as for Russian dataset, we used the probabilistic language model, described in BIBREF8.
Question: Is the RNN model evaluated against any baseline?

Ex Output:
Yes