You will be given a definition of a task first, then some input of the task.
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.

We see that parent quality is a simple yet effective feature and SVM model with this feature can achieve significantly higher ($p<0.001$) F1 score ($46.61\%$) than distance from the thesis and linguistic features. Although the BiLSTM model with attention and FastText baselines performs better than the SVM with distance from the thesis and linguistic features, it has similar performance to the parent quality baseline. We find that the flat representation of the context achieves the highest F1 score. It may be more difficult for the models with a larger number of parameters to perform better than the flat representation since the dataset is small. We also observe that modeling 3 claims on the argument path before the target claim achieves the best F1 score ($55.98\%$).
Question: How better are results compared to baseline models?
Output:
F1 score of best authors' model is 55.98 compared to BiLSTM and FastText that have F1 score slighlty higher than 46.61.