| Challenge: | Existing methods for detecting euphemisms are ineffective in impromptu euphorism detection . Existing approaches for e-mail detection are limited to word-level ephemismals . |
| Approach: | They propose a framework for impromptu euphemism detection that integrates context augmentation and multi-round iterative training to better predict the actual meaning of a masked token. |
| Outcome: | The proposed framework improves 76-fold over the previous state-of-the-art euphemism detector. |
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| Challenge: | euphemisms are ordinary-sounding words with a secret meaning that are used to conceal information . a primary motive of their use on social media is to evade content moderation efforts . |
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A Unified Generative Framework for Bilingual Euphemism Detection and Identification (2024.findings-acl)
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| Challenge: | Existing euphemism datasets are only domain-specific or language-specific. |
| Approach: | They propose a unified model to jointly conduct bilingual euphemism detection and identification tasks. |
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Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language (2023.emnlp-main)
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| Challenge: | Currently, only explicit abuse can be reliably detected due to the increasing amount of abusive language on the Web. |
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Explaining Matters: Leveraging Definitions and Semantic Expansion for Sexism Detection (2025.acl-long)
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| Challenge: | Existing tools for sexism detection fail to capture subtle distinctions within sexist content, limiting their practical applicability. |
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| Challenge: | Recent efforts to classify unstructured texts into specific types have been limited in practical scenarios. |
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Data Expansion Using WordNet-based Semantic Expansion and Word Disambiguation for Cyberbullying Detection (2022.lrec-1)
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| Challenge: | Existing methods to identify cyberbullying from text are limited due to the complexity of the content and the lack of labeled large-scale corpus. |
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SharedCon: Implicit Hate Speech Detection using Shared Semantics (2024.findings-acl)
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| Challenge: | Recent studies suggest that classifying hateful posts in a binary manner may not address nuanced task of detecting implicit hate speech. |
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Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks (2021.eacl-main)
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| Challenge: | Social networks face a major challenge in the form of rumors and fake news . rumor detection is suboptimal due to its rapidity and spread of information . |
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ImpliHateVid: A Benchmark Dataset and Two-stage Contrastive Learning Framework for Implicit Hate Speech Detection in Videos (2025.acl-long)
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| Challenge: | Existing studies on text-based hate speech detection focus on video-based approaches . however, hateful content remains a persistent challenge due to the vast amount of data generated every day. |
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Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning (2025.coling-main)
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| Challenge: | Existing methods for sarcasm detection lack commonsense inferential ability when faced with complex situations. |
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