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:
As explained in Section SECREF15 , the corruption introduced in Doc2VecC acts as a data-dependent regularization that suppresses the embeddings of frequent but uninformative words. In contrast, Doc2VecC manages to clamp down the representation of words frequently appear in the training set, but are uninformative, such as symbols and stop words. 
 Question: How do they determine which words are informative?
answer:
Informative are those that will not be suppressed by regularization performed.


question:
The dataset consists of more than $0.3$ million records and has been made available for future research. 
 Question: What is the size of the dataset?
answer:
$0.3$ million records


question:
Table 1 shows the official leaderboard on SQuAD test set when we submitted our system. Our model achieves a 68.73% EM score and 77.39% F1 score, which is ranked among the state of the art single models (without model ensembling). 
 Question: What is the exact performance on SQUAD?
answer:

Our model achieves a 68.73% EM score and 77.39% F1 score