Enhancing Online Grooming Detection via Backtranslation Augmentation (2025.coling-main)
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| Challenge: | Existing models to detect predatory conversations for online conversation platforms are lacking in real-world applications due to sparse distribution of predatory conversation data. |
| Approach: | They propose backtranslation augmentation to augment training datasets with more predatory conversations by using 3 neural translators to augment them. |
| Outcome: | The proposed model improves with fewer training epochs for better classification efficacy on 8 languages from 4 language families and shows that it is more efficient than previous models. |
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Don’t Augment, Rewrite? Assessing Abusive Language Detection with Synthetic Data (2024.findings-acl)
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| Challenge: | Existing datasets for abusive language detection and content moderation are limited by regulatory bodies and social media platforms. |
| Approach: | They propose to replace existing datasets in English with synthetic data by rewriting original texts with an instruction-based generative model. |
| Outcome: | The proposed model improves performance in cross-dataset training. |
Generation-Based Data Augmentation for Offensive Language Detection: Is It Worth It? (2023.eacl-main)
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| Challenge: | generative data augmentation has been shown to be effective in offensive language detection but the potential for bias injection has not been investigated. |
| Approach: | They propose to investigate the robustness of models trained on generated data in a variety of data augmentation setups and analyze models using the HateCheck suite. |
| Outcome: | The proposed model training setups on four English offensive language datasets are robust and robust, while the generative DA setups do not present bias injection issues. |
Abusive language in Spanish children and young teenager’s conversations: data preparation and short text classification with contextual word embeddings (2020.lrec-1)
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| Challenge: | Existing studies on how to automatically detect abusive short texts are gaining interest in the natural language processing community. |
| Approach: | They propose to use a contextual word embedding model to automatically detect abusive short texts for Spanish language. |
| Outcome: | The proposed model outperforms classical methods in the detection of abusive short texts for the spanish language. |
Early Detection of Sexual Predators in Chats (2021.acl-long)
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| Challenge: | Prior work has attempted to identify grooming chats but only after an incidence has already happened in the context of legal prosecution. |
| Approach: | They propose to analyze a running chat and predict grooming attempts as early as possible . they propose to use a new dataset to evaluate the problem from the point of view of prevention . |
| Outcome: | The proposed model is based on existing datasets and their limitations . it can be used to predict grooming attempts as early as possible . |
Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding (2021.emnlp-main)
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| Challenge: | Dialogue systems that generate factually incorrect responses are often unfitful and hallucinate factuality invalid. |
| Approach: | They propose a method to improve faithfulness and reduce hallucination of neural dialogue systems to known facts supplied by a Knowledge Graph. |
| Outcome: | The proposed approach improves faithfulness and reduces hallucination of dialogue systems to known facts . it leverages a token-level fact critic to identify plausible sources of hallucinism . |
Giving Control Back to Models: Enabling Offensive Language Detection Models to Autonomously Identify and Mitigate Biases (2024.findings-emnlp)
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| Challenge: | Existing models often rely on specific words to predict offensive content, compromising model fairness and potentially exacerbates biases against vulnerable and minority groups. |
| Approach: | They propose a bias self-awareness and data self-iteration framework to help models identify and mitigate biases by integrating multiple natural language processing techniques. |
| Outcome: | The proposed framework reduces false positive rate of models in in-distribution and out-of-difference tests, enhances model accuracy and fairness, and shows promising performance improvements on larger datasets. |
Boosting Text Augmentation via Hybrid Instance Filtering Framework (2023.findings-acl)
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| Challenge: | Existing text augmentation methods generate instances with shifted feature spaces, which leads to a drop in performance on large datasets. |
| Approach: | They propose a hybrid instance-filtering framework that generates instances with shifted feature spaces, which leads to a drop in performance on augmented data. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three classification tasks and nine public datasets. |
Exploring Data Augmentation Strategies for Hate Speech Detection in Roman Urdu (2022.lrec-1)
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| Challenge: | a number of social media platforms are generating hateful content, a new study finds . augmentation techniques are needed to improve the performance of the models . |
| Approach: | They evaluate different data augmentation techniques for the improvement of hate speech detection in Roman Urdu. |
| Outcome: | The proposed techniques improve hate speech detection in Roman Urdu on two datasets. |
Offensive Content Detection via Synthetic Code-Switched Text (2022.coling-1)
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| Challenge: | Existing methods to detect offensive content in social media platforms are limited by the availability of labeled code-switched data. |
| Approach: | They propose a method for generating synthetic code-switched offensive content data using human-generated data and a keyword classification baseline. |
| Outcome: | The proposed algorithm can be used to generate synthetic code-switched offensive content data and train it on human-generated data. |
HARALD: Augmenting Hate Speech Data Sets with Real Data (2022.findings-emnlp)
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| Challenge: | Hate speech detection depends on the availability of variable labeled data. |
| Approach: | They propose a method that uses real unlabelled data from online platforms to augment existing models by harvesting and processing it. |
| Outcome: | The proposed approach improves the classification performance of hate speech classification models. |