通过主题建模与因果干预，提升反事实检测模型的准确性与鲁棒性。
Topic-aware Causal Intervention for Counterfactual Detection
- 引入神经主题模型捕捉语句全局语义
- 通过因果干预平衡类别标签影响，提升检测性能
- 适合需要高可靠性反事实识别的任务场景
反事实陈述描述了未发生或不可能发生的事件，对众多自然语言处理应用具有重要意义。为此，本文研究反事实检测（CFD）问题并致力于提升现有模型性能。以往模型依赖提示词来判断反事实性，在测试阶段若无提示词则性能显著下降，且倾向于将反事实误判为非反事实。为解决这些问题，本文提出将神经主题模型融入CFD模型，以捕捉输入语句的全局语义；同时对模型隐层表示进行因果干预，平衡类别标签的影响。大量实验表明，该方法在反事实检测及其他敏感偏差任务上均优于现有最先进方法。
原文摘要 · Abstract (English)
Counterfactual statements, which describe events that did not or cannot take place, are beneficial to numerous NLP applications. Hence, we consider the problem of counterfactual detection (CFD) and seek to enhance the CFD models. Previous models are reliant on clue phrases to predict counterfactuality, so they suffer from significant performance drop when clue phrase hints do not exist during testing. Moreover, these models tend to predict non-counterfactuals over counterfactuals. To address these issues, we propose to integrate neural topic model into the CFD model to capture the global semantics of the input statement. We continue to causally intervene the hidden representations of the CFD model to balance the effect of the class labels. Extensive experiments show that our approach outperforms previous state-of-the-art CFD and bias-resolving methods in both the CFD and other bias-sensitive tasks.
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