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: The results indicate that most performance gains come from words embeddings, style, and morality features. Other features (emotion and sentiment) show lower importance: nevertheless, they still improve the overall system performance (on average 0.35% Macro-F$_1$ improvement) 
 Question: Based on this paper, what is the more predictive set of features to detect fake news?

Answer: words embeddings, style, and morality features


Question: To better analyze model generalization to an unseen, new domain as well as model leveraging the out-of-domain sources, we propose a new architecture which is an extension of the ARED model. In order to better select, aggregate and control the semantic information, a Refinement Adjustment LSTM-based component (RALSTM) is introduced to the decoder side. The proposed model can learn from unaligned data by jointly training the sentence planning and surface realization to produce natural language sentences.  
 Question: What is the difference of the proposed model with a standard RNN encoder-decoder?

Answer: Introduce a "Refinement Adjustment LSTM-based component" to the decoder


Question: Pretraining. We collect three years of online news articles from June 2016 to June 2019. We filter out articles overlapping with the evaluation data on media domain and time range. We then conduct several data cleaning strategies. 
 Question: What did they pretrain the model on?

Answer:
hree years of online news articles from June 2016 to June 2019