Topic-aware Causal Intervention for Counterfactual Detection

• URL: http://arxiv.org/abs/2409.16668v2
• Date: Mon, 30 Sep 2024 02:34:16 GMT
• Title: Topic-aware Causal Intervention for Counterfactual Detection
• Authors: Thong Nguyen, Truc-My Nguyen,
• Abstract summary: 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. We propose to integrate neural topic model into the CFD model to capture the global semantics of the input statement.
• Score: 7.295153420065765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• A PAC-Bayesian Perspective on the Interpolating Information Criterion [54.548058449535155]
  We show how a PAC-Bayes bound is obtained for a general class of models, characterizing factors which influence performance in the interpolating regime. We quantify how the test error for overparameterized models achieving effectively zero training error depends on the quality of the implicit regularization imposed by e.g. the combination of model, parameter-initialization scheme.
  arXiv  Detail & Related papers  (2023-11-13T01:48:08Z)
• Latent Diffusion Counterfactual Explanations [28.574246724214962]
  We introduce Latent Diffusion Counterfactual Explanations (LDCE) LDCE harnesses the capabilities of recent class- or text-conditional foundation latent diffusion models to expedite counterfactual generation. We show how LDCE can provide insights into model errors, enhancing our understanding of black-box model behavior.
  arXiv  Detail & Related papers  (2023-10-10T14:42:34Z)
• Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder [28.94606676886985]
  Front-door adjustment is a practical approach for dealing with unobserved confounding variables. We develop the theorem that guarantees the causal effect identifiability of CFD adjustment. We propose CFDiVAE to learn the representation of the CFD adjustment variable directly from data.
  arXiv  Detail & Related papers  (2023-10-03T10:24:44Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models [72.93652777646233]
  Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. We propose a new paradigm that treats COD as a conditional mask-generation task leveraging diffusion models. Our method, dubbed CamoDiffusion, employs the denoising process of diffusion models to iteratively reduce the noise of the mask.
  arXiv  Detail & Related papers  (2023-05-29T07:49:44Z)
• Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries [10.818661865303518]
  We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting. We introduce diffusion-based causal models (DCM) to learn causal mechanisms, that generate unique latent encodings. Our empirical evaluations demonstrate significant improvements over existing state-of-the-art methods for answering causal queries.
  arXiv  Detail & Related papers  (2023-02-02T04:08:08Z)
• Diffusion Causal Models for Counterfactual Estimation [18.438307666925425]
  We consider the task of counterfactual estimation from observational imaging data given a known causal structure. We propose Diff-SCM, a deep structural causal model that builds on recent advances of generative energy-based models. We find that Diff-SCM produces more realistic and minimal counterfactuals than baselines on MNIST data and can also be applied to ImageNet data.
  arXiv  Detail & Related papers  (2022-02-21T12:23:01Z)
• Behind the Scenes: An Exploration of Trigger Biases Problem in Few-Shot Event Classification [24.598938900747186]
  Few-Shot Event Classification (FSEC) aims at developing a model for event prediction, which can generalize to new event types with a limited number of annotated data. We find existing FSEC models suffer from trigger biases that signify the statistical homogeneity between some trigger words and target event types. To cope with the context-bypassing problem in FSEC models, we introduce adversarial training and trigger reconstruction techniques.
  arXiv  Detail & Related papers  (2021-08-29T13:46:42Z)
• Variational Causal Networks: Approximate Bayesian Inference over Causal Structures [132.74509389517203]
  We introduce a parametric variational family modelled by an autoregressive distribution over the space of discrete DAGs. In experiments, we demonstrate that the proposed variational posterior is able to provide a good approximation of the true posterior.
  arXiv  Detail & Related papers  (2021-06-14T17:52:49Z)
• Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations [64.85696493596821]
  In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction. We propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss. Our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-18T12:57:34Z)
• Explaining and Improving Model Behavior with k Nearest Neighbor Representations [107.24850861390196]
  We propose using k nearest neighbor representations to identify training examples responsible for a model's predictions. We show that kNN representations are effective at uncovering learned spurious associations. Our results indicate that the kNN approach makes the finetuned model more robust to adversarial inputs.
  arXiv  Detail & Related papers  (2020-10-18T16:55:25Z)

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.