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]: To account for sequences of words and characters that might carry useful information, we extracted word unigrams, bigrams, and trigrams as features. 
 Question: Which features do they use to model Twitter messages?
[A]: word unigrams, bigrams, and trigrams


[Q]: We train our models on Sentiment140 and Amazon product reviews. Both of these datasets concentrates on sentiment represented by a short text.  For training the softmax model, we divide the text sentiment to two kinds of emotion, positive and negative. And for training the tanh model, we convert the positive and negative emotion to [-1.0, 1.0] continuous sentiment score, while 1.0 means positive and vice versa.  
 Question: Was the introduced LSTM+CNN model trained on annotated data in a supervised fashion?
[A]:
Yes