Adversarial Multi-task Learning for End-to-end Metaphor Detection
Shenglong Zhang Ying Liu
Metaphor detection (MD) suffers from limited training data. In this paper, we
started with a linguistic rule called Metaphor Identification Procedure and
then proposed a novel multi-task learning framework to transfer knowledge in
basic sense discrimination (BSD) to MD. BSD is constructed from word sense
disambiguation (WSD), which has copious amounts of data. We leverage
adversarial training to align the data distributions of MD and BSD in the same
feature space, so task-invariant representations can be learned. To capture
fine-grained alignment patterns, we utilize the multi-mode structures of MD and
BSD. Our method is totally end-to-end and can mitigate the data scarcity
problem in MD. Competitive results are reported on four public datasets. Our
code and datasets are available.