Nominal Metaphor Generation with Multitask Learning. (arXiv:2206.05195v2 [cs.CL] UPDATED)

Metaphor generation is a challenging task which can impact many downstream
tasks such as improving user satisfaction with dialogue systems and story
generation. This paper tackles the problem of Chinese nominal metaphor
generation by introducing a multitask metaphor generation framework with
self-training and metaphor identification mechanisms. Self-training addresses
the data scarcity issue of metaphor datasets. That is, instead of solely
relying on labelled metaphor datasets which are usually small in size,
self-training helps identify potential metaphors from a large-scale unlabelled …

arxiv generation learning multitask learning