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.

[Q]: 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?
[A]: They randomly sample sentences from Wikipedia that contains an object RC and add them to training data

[Q]: The corpus is freely available at the following link
Question: Is this dataset publicly available?
[A]: Yes

[Q]: We compare our model with several baselines including:

Attn seq2seq BIBREF22: A model with simple attention over the input context at each time step during decoding.

Ptr-UNK BIBREF23: Ptr-UNK is the model which augments a sequence-to-sequence architecture with attention-based copy mechanism over the encoder context.

KV Net BIBREF6: The model adopted and argumented decoder which decodes over the concatenation of vocabulary and KB entities, which allows the model to generate entities.

Mem2Seq BIBREF7: Mem2Seq is the model that takes dialogue history and KB entities as input and uses a pointer gate to control either generating a vocabulary word or selecting an input as the output.

DSR BIBREF9: DSR leveraged dialogue state representation to retrieve the KB implicitly and applied copying mechanism to retrieve entities from knowledge base while decoding.
Question: What were the baseline systems?
[A]:
Attn seq2seq Ptr-UNK KV Net Mem2Seq DSR