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.
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Question: Our pool of annotators is selected after several short training rounds, with up to 15 predicates per round, in which they received extensive personal feedback.
Question: How are workers trained?

Answer: extensive personal feedback

Question: After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset.
Question: How many sentence transformations on average are available per unique sentence in dataset?

Answer: 27.41 transformation on average of single seed sentence is available in dataset.

Question: As discussed in Section SECREF2 , a variety of features that measure salience of an entity in text are available from the NLP community. We reimplemented the ones in Dunietz and Gillick BIBREF11 . This includes a variety of features, e.g. positional features, occurrence frequency and the internal POS structure of the entity and the sentence it occurs in.
Question: What features are used to represent the salience and relative authority of entities?

Answer:
Salience features positional features, occurrence frequency and the internal POS structure of the entity and the sentence it occurs in.
The relative authority of entity features: comparative relevance of the news article to the different entities occurring in it.