Enhancing Hate Speech Classifiers through a Gradient-assisted Counterfactual Text Generation Strategy (2025.findings-emnlp)
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| Challenge: | Strong attribute control can distort meaning, while prioritizing semantic preservation may weaken attribute alignment. |
| Approach: | They propose a method that restricts accepted samples to text meeting a minimum BERTScore threshold and applies gradient-assisted proposal generation to improve attribute alignment. |
| Outcome: | a new method for counterfactual text generation improves attribute alignment and semantic preservation . the proposed method achieved the best macro F1-score in two of three test sets . |
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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. |
Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation (2023.findings-emnlp)
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| Challenge: | Existing methods for finding meaningful counterfactuals rely on human annotation or implicit label invariance . a small amount of human-annotated counterf actual data can generate a robust dataset with learned labels. |
| Approach: | They propose a framework that generates counterfactuals by actively sampling from regions of uncertainty and automatically labeling them with a learned auxiliary classifier. |
| Outcome: | The proposed framework generates a large number of diverse counterfactuals and labels them with a learned classifier. |
CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation (2022.findings-emnlp)
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| Challenge: | Prior work on counterfactual data augmentation only considered restricted classes of perturbations, limiting their effectiveness. |
| Approach: | They propose a retrieval-augmented framework for creating diverse counterfactual perturbations for CDA. |
| Outcome: | Experiments on natural language inference and sentiment analysis show that the proposed framework can be used to encourage diversity in manually authored perturbations. |
Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification (2021.emnlp-main)
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| Challenge: | Existing approaches to improve generalization ability by augmenting training data with synonymous examples or adding random noises to word embeddings cannot address spurious association problem. |
| Approach: | They propose an end-to-end reinforcement learning framework which jointly performs counterfactual data generation and dual sentiment classification. |
| Outcome: | The proposed framework outperforms strong data augmentation baselines on several benchmark sentiment classification datasets. |
Generating Counter Narratives against Online Hate Speech: Data and Strategies (2020.acl-main)
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| Challenge: | Hate Speech (HS) is a pervasive issue that spreads quickly and widely . research has focused on avoiding undesired effects that come with content moderation . |
| Approach: | They propose to use large scale unsupervised language models to generate responses to hate effectively using large scale models. |
| Outcome: | The proposed methods lack quality data and produce generic/repetitive responses. |
People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection (2023.emnlp-main)
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| Challenge: | Past work has shown that counterfactually augmented data (CADs) can improve models' performance on out-of-domain tests. |
| Approach: | They use Polyjuice, ChatGPT, and Flan-T5 to automatically generate CADs . they find that CAD generates a model that flips the original label with minimal changes . |
| Outcome: | The proposed model improves model robustness on out-of-domain test sets and individual data points. |
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis (2021.acl-long)
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| Challenge: | Existing approaches to improve performance of deep neural models are limited by the nature of spurious patterns in the data. |
| Approach: | They propose to use augmented data to generate spurious patterns in NLP models . they propose to generate counterfactual data for data augmentation and explanation . |
| Outcome: | The proposed approach improves performance on augmented data and on human-generated data. |
DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation (2022.acl-long)
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| Challenge: | Existing domain adaptation (DA) algorithms are not able to handle out-of-distribution examples due to the costly and labor-intensive data labeling process. |
| Approach: | They propose a controllable generation approach to deal with domain adaptation challenge by generating a domain-counterfactual textual example from an input text. |
| Outcome: | The proposed approach outperforms baselines and improves accuracy of state-of-the-art unsupervised DA algorithm. |
A Survey on Natural Language Counterfactual Generation (2024.findings-emnlp)
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| Challenge: | Recent advances in NLP are driven by a variety of Large Language Models (LLMs), such as GPT-3 (175B) and PaLM (540B). |
| Approach: | They propose a taxonomy that categorizes the methods into four groups and summarizes the metrics for evaluating the generation quality. |
| Outcome: | The proposed taxonomy categorizes the generation methods into four groups and summarizes the metrics for evaluating the quality. |
Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech (2021.findings-acl)
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| Challenge: | Off-the-shelf methods to generate hate speech are limited in that they generate repetitive and safe responses regardless of the hate speech. |
| Approach: | They propose a three-module pipeline approach to generate diverse and relevant counterspeech . they first generate various counterspeak candidates by a generative model, then filter ungrammatical ones using a BERT model . |
| Outcome: | The proposed pipeline generates diverse and relevant counterspeech responses on three datasets. |