TASK DEFINITION: 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.
PROBLEM: To compare our results, we use the provided baseline, which is a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs.  
 Question: What was the baseline model?

SOLUTION: a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs

PROBLEM:  Last, we evaluate our approaches in 9 commonly used text classification datasets. We evaluate our methods on several commonly used datasets whose themes range from sentiment, web-page, science to medical and healthcare. 
 Question: What NLP tasks do they consider?

SOLUTION: text classification for themes including sentiment, web-page, science, medical and healthcare

PROBLEM: Our WordDecoding (WDec) model achieves F1 scores that are $3.9\%$ and $4.1\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. Similarly, our PtrNetDecoding (PNDec) model achieves F1 scores that are $3.0\%$ and $1.3\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. 
 Question: How higher are F1 scores compared to previous work?

SOLUTION:
WordDecoding (WDec) model achieves F1 scores that are $3.9\%$ and $4.1\%$ higher than HRL on the NYT29 and NYT24 datasets respectively PtrNetDecoding (PNDec) model achieves F1 scores that are $3.0\%$ and $1.3\%$ higher than HRL on the NYT29 and NYT24 datasets respectively