  This paper describes our two-stage system for the Euphemism Detection shared
task hosted by the 3rd Workshop on Figurative Language Processing in
conjunction with EMNLP 2022. Euphemisms tone down expressions about sensitive
or unpleasant issues like addiction and death. The ambiguous nature of
euphemistic words or expressions makes it challenging to detect their actual
meaning within a context. In the first stage, we seek to mitigate this
ambiguity by incorporating literal descriptions into input text prompts to our
baseline model. It turns out that this kind of direct supervision yields
remarkable performance improvement. In the second stage, we integrate visual
supervision into our system using visual imageries, two sets of images
generated by a text-to-image model by taking terms and descriptions as input.
Our experiments demonstrate that visual supervision also gives a statistically
significant performance boost. Our system achieved the second place with an F1
score of 87.2%, only about 0.9% worse than the best submission.
