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1. Self-Supervised Euphemism Detection and Identification for Content Moderation

   Zhu, Wanzheng Zhu ; Gong, Hongyu ; Bansal, Rohan ; Weinberg, Zachary. ; Christin, Nicolas ; Fanti, Giulia ; Bhat, Suma ( January 2021 , 2021 IEEE Symposium on Security and Privacy (SP))

   null (Ed.)

   Fringe groups and organizations have a long history of using euphemisms---ordinary-sounding words with a secret meaning---to conceal what they are discussing. Nowadays, one common use of euphemisms is to evade content moderation policies enforced by social media platforms. Existing tools for enforcing policy automatically rely on keyword searches for words on a ``ban list'', but these are notoriously imprecise: even when limited to swearwords, they can still cause embarrassing false positives. When a commonly used ordinary word acquires a euphemistic meaning, adding it to a keyword-based ban list is hopeless: consider ``pot'' (storage container or marijuana?) or ``heater'' (household appliance or firearm?). The current generation of social media companies instead hire staff to check posts manually, but this is expensive, inhumane, and not much more effective. It is usually apparent to a human moderator that a word is being used euphemistically, but they may not know what the secret meaning is, and therefore whether the message violates policy. Also, when a euphemism is banned, the group that used it need only invent another one, leaving moderators one step behind. This paper will demonstrate unsupervised algorithms that, by analyzing words in their sentence-level context, can both detect words being used euphemistically, and identify the secret meaning of each word. Compared to the existing state of the art, which uses context-free word embeddings, our algorithm for detecting euphemisms achieves 30--400\% higher detection accuracies of unlabeled euphemisms in a text corpus. Our algorithm for revealing euphemistic meanings of words is the first of its kind, as far as we are aware. In the arms race between content moderators and policy evaders, our algorithms may help shift the balance in the direction of the moderators. 

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2. CAT s are Fuzzy PETs : A Corpus and Analysis of Potentially Euphemistic Terms

   Gavidia, M. ; Lee, P. ; Feldman, A. ; Peng, J. ( January 2022 , arXiv preprint arXiv:2205.02728.)

   Euphemisms have not received much attention in natural language processing, despite being an important element of polite and figurative language. Euphemisms prove to be a difficult topic, not only because they are subject to language change, but also because humans may not agree on what is a euphemism and what is not. Nevertheless, the first step to tackling the issue is to collect and analyze examples of euphemisms. We present a corpus of potentially euphemistic terms (PETs) along with example texts from the GloWbE corpus. Additionally, we present a subcorpus of texts where these PETs are not being used euphemistically, which may be useful for future applications. We also discuss the results of multiple analyses run on the corpus. Firstly, we find that sentiment analysis on the euphemistic texts supports that PETs generally decrease negative and offensive sentiment. Secondly, we observe cases of disagreement in an annotation task, where humans are asked to label PETs as euphemistic or not in a subset of our corpus text examples. We attribute the disagreement to a variety of potential reasons, including if the PET was a commonly accepted term (CAT). 

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3. FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms.

   Lee P., Shode I. ( June 2023 , 12th Joint Conference on Lexical and Computational Semantics (SEM 2023))

   Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context. In this study, we expand on the task in two ways. First, we annotate PETs for vagueness, a linguistic property associated with euphemisms, and find that transformers are generally better at classifying vague PETs, suggesting linguistic differences in the data that impact performance. Second, we present novel euphemism corpora in three different languages: Yoruba, Spanish, and Mandarin Chinese. We perform euphemism disambiguation experiments in each language using multilingual transformer models mBERT and XLM-RoBERTa, establishing preliminary results from which to launch future work. 

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4. Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms

   Gavidia, M. ( May 2022 , UnImplicit: The Second Workshop on Understanding Implicit and Underspecified Language, co-located with NAACL 2022, Seattle, USA)

   This paper presents a linguistically driven proof of concept for finding potentially euphemistic terms, or PETs. Acknowledging that PETs tend to be commonly used expressions for a certain range of sensitive topics, we make use of distributional similarities to select and filter phrase candidates from a sentence and rank them using a set of simple sentimentbased metrics. We present the results of our approach tested on a corpus of sentences containing euphemisms, demonstrating its efficacy for detecting single and multi-word PETs from a broad range of topics. We also discuss future potential for sentiment-based methods on this task. 

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5. CS_Morgan at ImageCLEFmedical 2022 Caption Task: Deep Learning Based Multi-Label Classification and Transformers for Concept Detection & Caption Prediction

   Rahman, Md M. ; Layode, O. ( August 2022 , CLEF 2022 – Conference and Labs of the Evaluation Forum, September 5–8, 2022, Bologna, Italy, CEUR Workshop Proceedings (CEUR-WS.org) Proceedings)

   Faggioli, G. ; Ferro, N. ; Hanbury, A. ; Potthast, M. (Ed.)

   This paper describes the participation of Morgan_CS in both Concept Detection and Caption Prediction tasks under the ImageCLEFmedical 2022 Caption task. The task required participants to automatically identifying the presence and location of relevant concepts and composing coherent captions for the entirety of an image in a large corpus which is a subset of the extended Radiology Objects in COntext (ROCO) dataset. Our implementation is motivated by using encoder-decoder based sequence-to-sequence model for caption and concept generation using both pre-trained Text and Vision Transformers (ViTs). In addition, the Concept Detection task is also considered as a multi concept labels classification problem where several deep learning architectures with “sigmoid” activation are used to enable multilabel classification with Keras. We have successfully submitted eight runs for the Concept Detection task and four runs for the Caption Prediction task. For the Concept Detection Task, our best model achieved an F1 score of 0.3519 and for the Caption Prediction Task, our best model achieved a BLEU Score of 0.2549 while using a fusion of Transformers. 

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