Detecting Euphemisms with Literal Descriptions and Visual Imagery (2022)

Abstract: 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.

## introduction recent advances in large pretrained language models allowed the computational linguistics community to tackle more knowledge-intensive tasks which require commonsense reasoning @xcite @xcite , and figurative language understanding @xcite . in this work, we focus on a figurative language understanding task called euphemism detection. euphemisms attempt to smooth harsh, impolite, or blunt expressions about taboo or sensitive topics like death and unemployment @xcite . for instance, when we speak of older people we often refer to senior citizens instead of a direct expression that can be seen as offensive. identifying euphemisms is challenging due to their natural ambiguity, i.e., the meaning of the term shifts depending on the context: 'over the hill' could either mean someone or something is physically over some hill (literal), or someone or something is old, past one's prime (figurative) @xcite . one cannot distinguish these two different senses without sufficient context. thus, these terms are referred as potentially euphemistic terms (pets) @xcite . here, we propose a two-stage method for the euphemism detection shared task hosted by the 3rd workshop on figurative language processing at emnlp 2022. in the first stage, we manually collect literal descriptions for each pet. we then incorporate these descriptions into input text prompts to help the model distinguish figurative from literal usage. we demonstrate that this kind of extraneous linguistic supervision improves a strong baseline by a large margin. in the second stage, we attempt to answer the question, "is visual supervision also useful to infer the meaning behind a pet?" to answer this question, we use a text-to-image model which takes terms and descriptions as input, and we generate two sets of images, which we denote as visual imageries. our experiments show that using visual imagery provides the best results. a paired t-test points out that the improvement is statistically significant. our qualitative analysis also suggests visual imageries are beneficial for analyzing pets. the rest of this paper is organized as follows. section 2 describes our proposed solution. in section 3, we share the details of our evaluation setup and design choices. section 4 reports our experimental results. in section 5, we briefly review the relevant literature. section 6 outlines our conclusions and discuss the limitations of our approach. ## approach in this section, we first formulate the euphemism detection task by describing a simple baseline model, and then explain how we extend it with the literal term descriptions and visual imagery. ## data and implementation data. the euphemism detection dataset consists of two separate splits for training and testing purposes with 1573 and 394 examples, respectively. the test split is unlabeled. the whole data includes 131 different pets. since there is no data supplied for validation, we reserve 20% of the training data for this purpose. we only select the sentences with pets and remove repetitive patterns of punctuation "@ @ @ ..." to decrease computational requirements by shortening the input language. we manually collect literal descriptions within 6 hours, and try to avoid impolite expressions like insults or slang phrases. implementation. we use deberta-v3 base and large as our language model @xcite . we generate the visual imageries i t and i d by using an open-source dall-e implementation @xcite . 2 the number of images per visual imagery k is set to 9. we extract visual imagery embeddings v t and v d using clip's vit-l/14 as our visual encoder @xcite . f p is a single linear layer, and we randomly initialize its weights. we use adam optimizer with weight decay @xcite . the learning rate is set to 5e -6 and 3e with deberta-v3-base and deberta-v3-large, respectively. we train our models for a maximum of 50 epochs using tesla v100s and mixed precision. a typical experiment takes less than one hour with a batch size of 16. due to the small dataset size, we perform multiple experiments and reserve a different portion of the labeled data for validation in each experiment. we report mean and standard deviation over all experiments, and use ensembling to evaluate our system on the test set. ## conclusion in this paper, we described our two-stage method for the euphemism detection task. we first collected literal descriptions for pets, inserted these descriptions into the model input, and showed that such linguistic supervision greatly boosts performance. we then supplied extra visual supervision using a text-to-image model, where we denote this kind of supervision as visual imageries. we achieved a statistically significant performance increase by using visual imageries in addition to the term descriptions. our qualitative analysis on visual imageries also suggests that a text-to-image model can be a functional tool to break down how models interpret figures of speech. limitations. due to working with a small-scale dataset, we were able to manually collect descriptions for the pets. collecting these descriptions using an automatic retrieval system would be more sophisticated. we also did not perform a detailed analyses of the results, which could help shed light on the contribution of each model component.