  Despite their importance in training artificial intelligence systems, large
datasets remain challenging to acquire. For example, the ImageNet dataset
required fourteen million labels of basic human knowledge, such as whether an
image contains a chair. Unfortunately, this knowledge is so simple that it is
tedious for human annotators but also tacit enough such that they are
necessary. However, human collaborative efforts for tasks like labeling massive
amounts of data are costly, inconsistent, and prone to failure, and this method
does not resolve the issue of the resulting dataset being static in nature.
What if we asked people questions they want to answer and collected their
responses as data? This would mean we could gather data at a much lower cost,
and expanding a dataset would simply become a matter of asking more questions.
We focus on the task of Visual Question Answering (VQA) and propose a system
that uses Visual Question Generation (VQG) to produce questions, asks them to
social media users, and collects their responses. We present two models that
can then parse clean answers from the noisy human responses significantly
better than our baselines, with the goal of eventually incorporating the
answers into a Visual Question Answering (VQA) dataset. By demonstrating how
our system can collect large amounts of data at little to no cost, we envision
similar systems being used to improve performance on other tasks in the future.
