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:
Metrics. We use tolerance accuracy BIBREF16, which measures how far away the predicted span is from the gold standard span, as a metric. The rationale behind the metric is that, in practice, it suffices to recommend a rough span which contains the answer – a difference of a few seconds would not matter much to the user. Metrics. We used accuracy and MRR (Mean Reciprocal Ranking) as metrics.  Metrics. To evaluate our pipeline approach we use overall accuracy after filtering and accuracy given that the segment is in the top 10 videos.  
 Question: What evaluation metrics were used in the experiment?
answer:
For sentence-level prediction they used tolerance accuracy, for segment retrieval accuracy and MRR and for the pipeline approach they used overall accuracy


question:
This model uses a particular RNN cell in order to store just relevant information about the given question.  Dynamic Memory stores information of entities present in $T$ . The addition of the $s_t^T q$ term in the gating function is our main contribution.  
 Question: How does the model recognize entities and their relation to answers at inference time when answers are not accessible?
answer:
gating function Dynamic Memory


question:
The ensembles were formed by simply averaging the predictions from the constituent single models. 
 Question: How does their ensemble method work?
answer:

simply averaging the predictions from the constituent single models