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.
--------
Question: We also looked at where there was complete agreement by all annotators that a triple extraction was incorrect. In total there were 138 of these triples originating from 76 unique sentences.  
 Question: What is the most common error type?

Answer: all annotators that a triple extraction was incorrect


Question: . While automated evaluation metrics like ROUGE measure lexical similarity between machine and human summaries, humans can better measure how coherent and readable a summary is. Our evaluation study investigates whether tuning the PG-net model increases summary coherence, by asking evaluators to select which of three summaries for the same document they like most: the PG-net model trained on CNN/DM; the model trained on student reflections; and finally the model trained on CNN/DM and tuned on student reflections. 20 evaluators were recruited from our institution and asked to each perform 20 annotations. Summaries are presented to evaluators in random order. Evaluators are then asked to select the summary they feel to be most readable and coherent. 
 Question: Who were the human evaluators used?

Answer: 20 evaluators were recruited from our institution and asked to each perform 20 annotations


Question: The resulting set of 46 documents makes up our base corpus. Note that these documents vary greatly in size, which means the resulting corpus is varied, but not balanced in terms of size (Table TABREF43). 
 Question: How big PIE datasets are obtained from dictionaries?

Answer:
46 documents makes up our base corpus