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.

Example Input: The annotation process was a trial-and-error, with cycles composed of annotation, discussing confusing entities, updating the annotation guide schematic and going through the corpus section again to correct entities following guide changes.
Question: Did they experiment with the corpus?
Example Output: Yes

Example Input: In this paper, we present an extraction-then-synthesis framework for machine reading comprehension shown in Figure 1 , in which the answer is synthesized from the extraction results. We build an evidence extraction model to predict the most important sub-spans from the passages as evidence, and then develop an answer synthesis model which takes the evidence as additional features along with the question and passage to further elaborate the final answers.
Question: Which framework they propose in this paper?
Example Output: extraction-then-synthesis framework

Example Input: Training Dataset UM Inventory BIBREF5 is a public dataset created by researchers from the University of Minnesota, containing about 37,500 training samples with 75 abbreviation terms. Existing work reports abbreviation disambiguation results on 50 abbreviation terms BIBREF6, BIBREF5, BIBREF17. However, after carefully reviewing this dataset, we found that it contains many samples where medical professionals disagree: wrong samples and uncategorized samples. Due to these mistakes and flaws of this dataset, we removed the erroneous samples and eventually selected 30 abbreviation terms as our training dataset that can be made public.
Question: What existing dataset is re-examined and corrected for training?
Example Output:
UM Inventory