TASK DEFINITION: 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.
PROBLEM: Probing Methodology and Modeling ::: Task Definition and Modeling ::: Baselines and Sanity Checks.
When creating synthetic datasets, it is important to ensure that systematic biases, or annotation artifacts BIBREF41, are not introduced into the resulting probes and that the target datasets are sufficiently challenging (or good, in the sense of BIBREF42). To test for this, we use several of the MCQA baseline models first introduced in BIBREF0, which take inspiration from the LSTM-based models used in BIBREF43 for NLI and various partial-input baselines based on these models. 
 Question: How do they control for annotation artificats?

SOLUTION:  we use several of the MCQA baseline models first introduced in BIBREF0

PROBLEM: VQA research began in earnest in late 2014 when the DAQUAR dataset was released BIBREF0 
 Question: From when are many VQA datasets collected?

SOLUTION: late 2014

PROBLEM: As shown in Table TABREF3 , we collect Amazon review keywords for 2,896 e-books (publishers: Kiwi, Rowohlt, Fischer, and Droemer), which leads to 33,663 distinct review keywords and on average 30 keyword assignments per e-book. 
 Question: how large is the vocabulary?

SOLUTION:
33,663