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:
One example illustrates that the mistakes the model makes can be associated with changes in the party policy. 
 Question: Do changes in policies of the political actors account for all of the mistakes the model made?
answer:
Yes


question:
In our experiments we use the same data-sets from other works BIBREF32, BIBREF23, BIBREF33 as well as other datasets that we collected by us using a similar criterion (described in Section SECREF4). We use thirty different discussions that took place between March 2015 and June 2019, half of them with controversy and half without it. We considered discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia. We also studied these discussions taking first 140 characters and then 280 from each tweet to analyze the difference in performance and computing time wrt the length of the posts. 
 Question: What datasets did they use?
answer:
BIBREF32, BIBREF23, BIBREF33 discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia. 


question:
TF-IDF: We applied TF-IDF features as inputs to four classifiers: support vector machine classifier (SVM), logistic regression classifier (LR), Naive Bayes classifier (NB) and random forest (RF); CNN: We followed the same architecture as BIBREF9; LSTM: We applied an LSTM model BIBREF20 to classify the sentences with pre-trained word embeddings;LSTM-soft: We then added a soft-attention BIBREF21 layer on top of the LSTM model where we computed soft alignment scores over each of the hidden states; LSTM-self: We applied a self-attention layer BIBREF22 to LSTM model.  
 Question: To what baseline models is proposed model compared?
answer:

support vector machine classifier (SVM) logistic regression classifier (LR) Naive Bayes classifier (NB) random forest (RF) CNN LSTM  LSTM-soft LSTM-self