  Hyperbole and metaphor are common in day-to-day communication (e.g., "I am in
deep trouble": how does trouble have depth?), which makes their detection
important, especially in a conversational AI setting. Existing approaches to
automatically detect metaphor and hyperbole have studied these language
phenomena independently, but their relationship has hardly, if ever, been
explored computationally. In this paper, we propose a multi-task deep learning
framework to detect hyperbole and metaphor simultaneously. We hypothesize that
metaphors help in hyperbole detection, and vice-versa. To test this hypothesis,
we annotate two hyperbole datasets- HYPO and HYPO-L- with metaphor labels.
Simultaneously, we annotate two metaphor datasets- TroFi and LCC- with
hyperbole labels. Experiments using these datasets give an improvement of the
state of the art of hyperbole detection by 12%. Additionally, our multi-task
learning (MTL) approach shows an improvement of up to 17% over single-task
learning (STL) for both hyperbole and metaphor detection, supporting our
hypothesis. To the best of our knowledge, ours is the first demonstration of
computational leveraging of linguistic intimacy between metaphor and hyperbole,
leading to showing the superiority of MTL over STL for hyperbole and metaphor
detection.
