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.

Q: We collect three years of online news articles from June 2016 to June 2019. 
 Question: What unlabeled corpus did they use?

A: three years of online news articles from June 2016 to June 2019
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Q: 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?

A: For sentence-level prediction they used tolerance accuracy, for segment retrieval accuracy and MRR and for the pipeline approach they used overall accuracy
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Q: Based on the gained insights, we have improved the state-of-the-art in XNLI for the Translate-Test and Zero-Shot approaches by a substantial margin. 
 Question: Is the improvement over state-of-the-art statistically significant?

A:
Yes
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