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: In this work, we use pretraining on the unlabeled data of each task and show that it can increase the performance of classification systems. 
 Question: Where is MVCNN pertained?

A: on the unlabeled data of each task
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Q: The best baseline model is NO-MOVE, reaching an accuracy of 30.3% on single sentences and 0.3 on complete paragraphs. 
 Question: How well did the baseline perform?

A: accuracy of 30.3% on single sentences and 0.3 on complete paragraphs
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Q: Secondly, texts go through a cascade of annotation tools, enriching it with the following information:

Morphosyntactic interpretations (sets of tags), using Morfeusz 0.82 BIBREF25 ,

Tagging (selection of the most probable interpretation), using a transformation-based learning tagger, PANTERA 0.9.1 BIBREF26 ,

Syntactic groups (possibly nested) with syntactic and semantic heads, using a rule-based shallow parser Spejd 1.3.7 BIBREF27 with a Polish grammar, including improved version of modifications by BIBREF28 , enabling lemmatisation of nominal syntactic groups,

Named entities, using two available tools: NERF 0.1 BIBREF29 and Liner2 2.3 BIBREF30 .

 
 Question: How is the data in RAFAEL labelled?

A:
Using a set of annotation tools such as Morfeusz, PANTERA, Spejd, NERF and Liner
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