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.

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?
Informative are those that will not be suppressed by regularization performed.

Our system, including software and corpus, is available as an open source project for free research purpose and we believe that it is a good baseline for the development and comparison of future Vietnamese SRL systems.  
 Question: Are their corpus and software public?
Yes

Therefore, we implement reinforcement learning and elaborately design two rewards to describe the irony accuracy and sentiment preservation, respectively. 
 Question: What is the combination of rewards for reinforcement learning?

irony accuracy sentiment preservation