  IBM Watson is a cognitive computing system capable of question answering in
natural languages. It is believed that IBM Watson can understand large corpora
and answer relevant questions more effectively than any other
question-answering system currently available. To unleash the full power of
Watson, however, we need to train its instance with a large number of
well-prepared question-answer pairs. Obviously, manually generating such pairs
in a large quantity is prohibitively time consuming and significantly limits
the efficiency of Watson's training. Recently, a large-scale dataset of over 30
million question-answer pairs was reported. Under the assumption that using
such an automatically generated dataset could relieve the burden of manual
question-answer generation, we tried to use this dataset to train an instance
of Watson and checked the training efficiency and accuracy. According to our
experiments, using this auto-generated dataset was effective for training
Watson, complementing manually crafted question-answer pairs. To the best of
the authors' knowledge, this work is the first attempt to use a large-scale
dataset of automatically generated question-answer pairs for training IBM
Watson. We anticipate that the insights and lessons obtained from our
experiments will be useful for researchers who want to expedite Watson training
leveraged by automatically generated question-answer pairs.
