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.

[EX Q]: The ensembles were formed by simply averaging the predictions from the constituent single models. 
 Question: How does their ensemble method work?
[EX A]: simply averaging the predictions from the constituent single models

[EX Q]: We have evaluated our models considering the F1 Score, which is the harmonic mean of precision and recall. We have run ten times the experiment for each model and considered the average F1 Score. The results are mentioned in Table TABREF11. Considering F1 Macro the models that include the multi-attention mechanism outperform the others and particularly the one with the Projected Layer has the highest performance. In three out of four pairs of models, the ones with the Projected Layer achieved better performance, so in most cases the addition of the Projected Layer had a significant enhancement.

 
 Question: Which variation provides the best results on this dataset?
[EX A]: the model with multi-attention mechanism and a projected layer

[EX Q]: (1) AutoJudge consistently and significantly outperforms all the baselines, including RC models and other neural text classification models, which shows the effectiveness and robustness of our model. (2) RC models achieve better performance than most text classification models (excluding GRU+Attention), which indicates that reading mechanism is a better way to integrate information from heterogeneous yet complementary inputs. (3) Comparing with conventional RC models, AutoJudge achieves significant improvement with the consideration of additional law articles. 
 Question: what are their results on the constructed dataset?
[EX A]:
AutoJudge consistently and significantly outperforms all the baselines RC models achieve better performance than most text classification models (excluding GRU+Attention) Comparing with conventional RC models, AutoJudge achieves significant improvement