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.

本研究介绍了我们参加 EMNLP 2022 与第三届比喻语言处理研讨会所主办的委婉语探测共享任务的两阶段系统。该系统利用直接督导和视觉督导，同时考虑了直接描述和词汇生成的图像，发现这两种方法都可以显著提高系统性能。最终我们的系统得分为 87.2%，仅比最佳提交差 0.9%。