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]: In this experiment set, our BERT implementation is compared to several systems that participated in the MEDDOCAN challenge: a CRF classifier BIBREF18, a spaCy entity recogniser BIBREF18, and NLNDE BIBREF12, the winner of the shared task and current state of the art for sensitive information detection and classification in Spanish clinical text.  However, attending to the obtained results, BERT remains only 0.3 F1-score points behind, and would have achieved the second position among all the MEDDOCAN shared task competitors. Taking into account that only 3% of the gold labels remain incorrectly annotated, the task can be considered almost solved, and it is not clear if the differences among the systems are actually significant, or whether they stem from minor variations in initialisation or a long-tail of minor labelling inconsistencies.
[EX A]: Does BERT reach the best performance among all the algorithms compared?

[EX Q]: The ensembles were formed by simply averaging the predictions from the constituent single models.
[EX A]: How does their ensemble method work?

[EX Q]: We built a dataset for the task of hate speech detection in memes with 5,020 images that were weakly labeled into hate or non-hate memes, depending on their source.
[EX A]:
How is each instance of the dataset annotated?