以字面描述和视觉图像探测委婉 (Detecting Euphemisms with Literal Descriptions and Visual Imagery)

from arxiv, 7 pages, 1 table, 1 figure. Accepted to the 3rd Workshop on Figurative Language Processing at EMNLP 2022. https://github.com/ilkerkesen/euphemism

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.

翻译：本文描述了我们与EMNLP 2022 联合主办的关于模拟语言处理的第三次讲习班共同任务的两阶段的“委婉感探测”系统。委婉感低调地表达诸如成瘾和死亡等敏感或不愉快的问题。委婉词或表达方式的模糊性使得难以在某种背景下辨别其实际含义。在第一阶段,我们试图通过将字面描述纳入输入文本以提示我们的基线模型来减轻这种模糊性。事实证明,这种直接监督产生了显著的绩效改进。在第二阶段,我们用视觉图像将视觉监督纳入我们的系统,这是用文字到图像模型生成的两套图像,用术语和描述作为投入。我们的实验表明,视觉监督也提供了具有统计意义的绩效提升。我们的系统取得了第二位,F1分为87.2%,仅比最佳提交者差0.9%左右。