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.

Ex Input:
Wikilinks can be seen as a large-scale, naturally-occurring, crowd-sourced dataset where thousands of human annotators provide ground truths for mentions of interest. This means that the dataset contains various kinds of noise, especially due to incoherent contexts. We prepare our dataset from the local-context version of Wikilinks, and resolve ground-truth links using a Wikipedia dump from April 2016. We use the page and redirect tables for resolution, and keep the database pageid column as a unique identifier for Wikipedia entities. We discard mentions where the ground-truth could not be resolved (only 3% of mentions). 
 Question: How was a quality control performed so that the text is noisy but the annotations are accurate?

Ex Output:
The authors believe that the Wikilinks corpus  contains ground truth annotations while being noisy. They discard mentions that cannot have ground-truth verified by comparison with Wikipedia.


Ex Input:
As the baseline, we simply judge the input token as IOCs on the basis of the spelling features described in BIBREF12 . 
 Question: What is used a baseline?

Ex Output:
As the baseline, we simply judge the input token as IOCs on the basis of the spelling features described in BIBREF12


Ex Input:
Our second submission obtains 82.4 $F_1$ on the official test set, and ranks $4th$ on this shared task. 
 Question: Where did this model place in the final evaluation of the shared task?

Ex Output:

$4th$