An Exploration of Linguistically-Driven and Transfer Learning Methods for Euphemism Detection (2022)

Abstract: Euphemisms are often used to drive rhetoric, but their automated recognition and interpretation are under-explored. We investigate four methods for detecting euphemisms in sentences containing potentially euphemistic terms. The first three linguistically-motivated methods rest on an understanding of (1) euphemism’s role to attenuate the harsh connotations of a taboo topic and (2) euphemism’s metaphorical underpinnings. In contrast, the fourth method follows recent innovations in other tasks and employs transfer learning from a general-domain pre-trained language model. While the latter method ultimately (and perhaps surprisingly) performed best (F1 = 0.74), we comprehensively evaluate all four methods to derive additional useful insights from the negative results.

## introduction euphemism is a ubiquitous figurative language tool, wherein the speaker refers to taboo topics in indirect, metaphorical terms to convey politeness or formality. identifying euphemism can reveal tacit facts about the speaker's intention and the context of the utterance @xcite , but there has been minimal work exploring how this might be done computationally @xcite . in this paper, we compare the performance of four methods for automated euphemism detection. the first two methods identify euphemism based on expected sentiment differences between euphemisms and their automatically generated non-euphemistic paraphrases. the third method exploits the metaphorical underpinnings of euphemism, following the hypothesis that the euphemism's root word will have more possible senses than its single-word literal paraphrase. in contrast to these linguistically-driven methods, the last method fine-tunes a popular pre-trained language model @xcite for the task of euphemism detection. we find strong, and perhaps surprising, evidence that the last method outperforms the alternatives. our contribution in this paper is twofold. first, we demonstrate the utility of pre-trained language models for novel figurative language processing tasks. second, we demonstrate our process of translating linguistic theory of euphemism into empirical models. although our results show that those methods need to be refined, it is our hope that this transparency will minimize redundancy in future research. thus, our work is well-aligned with @xcite 's position that reporting negative results in shared tasks can produce useful insights. ## dataset and task description our work was conducted as part of a shared task with the goal of creating a system that determines whether or not a given sentence containing a pet is euphemistic. the data was sourced from gavidia et al. (2022 defined as the sentence containing the pet along with the preceding and following sentences to provide additional context. utterances were assigned labels of 1 or 0, with 1 indicating that the pet was euphemistic and 0 indicating that it was not. a condensed example of an entry in the training dataset is shown in ## methods we explored four methods for euphemism detection, broadly categorized by their reliance on engineered, linguistically-driven features or transfer learning. in the first two methods, we expected that if the original sentence contained a euphemism, then substituting the pet with a synonymous noneuphemistic term should produce a difference in the sentiment between the original and the generated, paraphrased sentence. the third approach relies on the premise that euphemisms are metaphorical extensions of the head of the phrase, while their non-euphemistic paraphrases have more specific semantic scope. the fourth approach employs bert, a popular transformer-based model that we finetuned to detect euphemism. we provide further details regarding the intuition and implementation guiding each of these approaches in §4.1-4.3. ## evaluation we compared the performance of all methods using precision, recall, and f 1 -measure, following task guidelines. the sentiment-based methods described in §4.1 were excluded from our shared task submission and thus not evaluated on the test data, due to their observed under-performance during validation experiments. our validation experiments were evaluated using a withheld subset of 20% of the training data. in in ## discussion the results show that bert unquestionably outperforms the sentiment-and word sense-based methods. this illustrates that a fine-tuned model pretrained on general-domain data can be successfully leveraged for euphemism detection. close inspection of the predictions from the three linguisticallydriven methods revealed that they overwhelmingly classified sentences as euphemistic. we suspect that they learned to reliably detect the presence of figurative language but require further refinement to discriminate between euphemism and other figurative language phenomena (e.g., metaphor). sentiment-bt likely under-performed because we found that pets remained surprisingly intact through the process of back-translation. hence, there were few sentiment differences between the original and paraphrased sentences. similarly, the tokens selected in sentiment-mlm may have fit the sentence context but were not literal paraphrases of the pet. beyond sentiment-mlm, this may also explain the failure of word sense relative to bert. if the paraphrases themselves are unreliable, then it entails that subsequent downstream comparisons of sentiment or polysemy between the original and paraphrased sentences will also be inaccurate. ## conclusion in this paper, we explored linguistically-driven and transfer learning methods to detect euphemism. our linguistically-driven methods drew upon differences in sentiment and word sense frequency between euphemisms and their paraphrases. our transfer learning method fine-tuned bert for euphemism detection and proved to be the most successful. we motivate our sentiment-and word sense-based methods using linguistic theory and report their results despite under-performance to highlight the scope for future improvement. in our next steps, we aim to devise techniques for more accurately paraphrasing euphemisms (simultaneously driving the dial forward towards euphemism understanding), allowing us to further investigate linguistically-driven approaches. we will also study whether fine-tuning source models intended for metaphor detection or sentiment analysis will further improve upon our transfer learning results.