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: Based on annotated corpora and token-based features, studies used machine learning approaches to build word segmentation systems with accuracy about 94%-97%. 
 Question: How successful are the approaches used to solve word segmentation in Vietnamese?

A: Their accuracy in word segmentation is about 94%-97%.
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Q: The first part is described as Task A, the purpose of which is to identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set. We chose the best 3 results from each word embeddings group (EE, EP, EC) from Table TABREF19 presenting F1-scores for all models. Then we evaluated these results using more detailed measures for timexes, presented in BIBREF27 . 
 Question: What experiments are presented?

A: identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set  Then we evaluated these results using more detailed measures for timexes
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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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