Take its Essence, Discard its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect

• URL: http://arxiv.org/abs/2406.00983v1
• Date: Mon, 3 Jun 2024 04:34:30 GMT
• Title: Take its Essence, Discard its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect
• Authors: Junyu Lu, Bo Xu, Xiaokun Zhang, Kaiyuan Liu, Dongyu Zhang, Liang Yang, Hongfei Lin,
• Abstract summary: We propose a Counterfactual Causal Debiasing Framework (CCDF) to mitigate lexical bias in toxic language detection (TLD) CCDF preserves the "useful impact" of lexical bias and eliminates the "misleading impact"
• Score: 23.628565620485364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current methods of toxic language detection (TLD) typically rely on specific tokens to conduct decisions, which makes them suffer from lexical bias, leading to inferior performance and generalization. Lexical bias has both "useful" and "misleading" impacts on understanding toxicity. Unfortunately, instead of distinguishing between these impacts, current debiasing methods typically eliminate them indiscriminately, resulting in a degradation in the detection accuracy of the model. To this end, we propose a Counterfactual Causal Debiasing Framework (CCDF) to mitigate lexical bias in TLD. It preserves the "useful impact" of lexical bias and eliminates the "misleading impact". Specifically, we first represent the total effect of the original sentence and biased tokens on decisions from a causal view. We then conduct counterfactual inference to exclude the direct causal effect of lexical bias from the total effect. Empirical evaluations demonstrate that the debiased TLD model incorporating CCDF achieves state-of-the-art performance in both accuracy and fairness compared to competitive baselines applied on several vanilla models. The generalization capability of our model outperforms current debiased models for out-of-distribution data.

Related papers

• Applying Intrinsic Debiasing on Downstream Tasks: Challenges and Considerations for Machine Translation [19.06428714669272]
  We systematically test how methods for intrinsic debiasing affect neural machine translation models. We highlight three challenges and mismatches between the debiasing techniques and their end-goal usage.
  arXiv  Detail & Related papers  (2024-06-02T15:57:29Z)
• Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination [54.865941973768905]
  We propose a novel and practical bias mitigation method, CRISPR, to eliminate bias neurons of language models in instruction-following settings. CRISPR automatically determines biased outputs and categorizes neurons that affect the biased outputs as bias neurons using an explainability method. Experimental results demonstrate the effectiveness of our method in mitigating biases under zero-shot instruction-following settings without losing the model's task performance and existing knowledge.
  arXiv  Detail & Related papers  (2023-11-16T07:16:55Z)
• Debiasing should be Good and Bad: Measuring the Consistency of Debiasing Techniques in Language Models [9.90597427711145]
  Debiasing methods seek to mitigate the tendency of Language Models (LMs) to occasionally output toxic or inappropriate text. We propose a standardized protocol which distinguishes methods that yield not only desirable results, but are also consistent with their mechanisms and specifications. We show that our protocol provides essential insights into the generalizability and interpretability of debiasing methods that may otherwise go overlooked.
  arXiv  Detail & Related papers  (2023-05-23T17:45:54Z)
• On Bias and Fairness in NLP: Investigating the Impact of Bias and Debiasing in Language Models on the Fairness of Toxicity Detection [7.297345802761503]
  representation bias, selection bias and overamplification bias are investigated. We show that overamplification bias is the most impactful type of bias on the fairness of the task of toxicity detection. We introduce a list of guidelines to ensure the fairness of the task of toxicity detection.
  arXiv  Detail & Related papers  (2023-05-22T08:44:00Z)
• Cross Pairwise Ranking for Unbiased Item Recommendation [57.71258289870123]
  We develop a new learning paradigm named Cross Pairwise Ranking (CPR) CPR achieves unbiased recommendation without knowing the exposure mechanism. We prove in theory that this way offsets the influence of user/item propensity on the learning.
  arXiv  Detail & Related papers  (2022-04-26T09:20:27Z)
• Mitigating Biases in Toxic Language Detection through Invariant Rationalization [70.36701068616367]
  biases toward some attributes, including gender, race, and dialect, exist in most training datasets for toxicity detection. We propose to use invariant rationalization (InvRat), a game-theoretic framework consisting of a rationale generator and a predictor, to rule out the spurious correlation of certain syntactic patterns. Our method yields lower false positive rate in both lexical and dialectal attributes than previous debiasing methods.
  arXiv  Detail & Related papers  (2021-06-14T08:49:52Z)
• Challenges in Automated Debiasing for Toxic Language Detection [81.04406231100323]
  Biased associations have been a challenge in the development of classifiers for detecting toxic language. We investigate recently introduced debiasing methods for text classification datasets and models, as applied to toxic language detection. Our focus is on lexical (e.g., swear words, slurs, identity mentions) and dialectal markers (specifically African American English)
  arXiv  Detail & Related papers  (2021-01-29T22:03:17Z)
• Improving Robustness by Augmenting Training Sentences with Predicate-Argument Structures [62.562760228942054]
  Existing approaches to improve robustness against dataset biases mostly focus on changing the training objective. We propose to augment the input sentences in the training data with their corresponding predicate-argument structures. We show that without targeting a specific bias, our sentence augmentation improves the robustness of transformer models against multiple biases.
  arXiv  Detail & Related papers  (2020-10-23T16:22:05Z)
• Towards Robustifying NLI Models Against Lexical Dataset Biases [94.79704960296108]
  This paper explores both data-level and model-level debiasing methods to robustify models against lexical dataset biases. First, we debias the dataset through data augmentation and enhancement, but show that the model bias cannot be fully removed via this method. The second approach employs a bag-of-words sub-model to capture the features that are likely to exploit the bias and prevents the original model from learning these biased features.
  arXiv  Detail & Related papers  (2020-05-10T17:56:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.