In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

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

[EX Q]: In all our experiments, we used the out-of-the-box BERT models without any task-specific fine-tuning.
[EX A]: How does their model differ from BERT?

[EX 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 .


[EX A]:
How is the data in RAFAEL labelled?