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.

Input: Consider Input: 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?

Output: the model with multi-attention mechanism and a projected layer


Input: Consider Input: Both the audio and the transcript are de-identified (by removing the identifying information) with digital zeros and [de-identified] tags, respectively. 
 Question: Is the data de-identified?

Output: Yes


Input: Consider Input: We notice that the concatenation consistently outperforms the gated-attention mechanism for both training and testing instructions. We suspect that the gated-attention is useful in the scenarios where objects are described in terms of multiple attributes, but it has no to harming effect when it comes to the order connectors. 
 Question: Why they conclude that the usage of Gated-Attention provides no competitive advantage against concatenation in this setting?


Output: concatenation consistently outperforms the gated-attention mechanism for both training and testing instructions