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Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment (2403.02698v1)

Published 5 Mar 2024 in cs.CL

Abstract: Conventional multi-hop fact verification models are prone to rely on spurious correlations from the annotation artifacts, leading to an obvious performance decline on unbiased datasets. Among the various debiasing works, the causal inference-based methods become popular by performing theoretically guaranteed debiasing such as casual intervention or counterfactual reasoning. However, existing causal inference-based debiasing methods, which mainly formulate fact verification as a single-hop reasoning task to tackle shallow bias patterns, cannot deal with the complicated bias patterns hidden in multiple hops of evidence. To address the challenge, we propose Causal Walk, a novel method for debiasing multi-hop fact verification from a causal perspective with front-door adjustment. Specifically, in the structural causal model, the reasoning path between the treatment (the input claim-evidence graph) and the outcome (the veracity label) is introduced as the mediator to block the confounder. With the front-door adjustment, the causal effect between the treatment and the outcome is decomposed into the causal effect between the treatment and the mediator, which is estimated by applying the idea of random walk, and the causal effect between the mediator and the outcome, which is estimated with normalized weighted geometric mean approximation. To investigate the effectiveness of the proposed method, an adversarial multi-hop fact verification dataset and a symmetric multi-hop fact verification dataset are proposed with the help of the LLM. Experimental results show that Causal Walk outperforms some previous debiasing methods on both existing datasets and the newly constructed datasets. Code and data will be released at https://github.com/zcccccz/CausalWalk.

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Authors (3)
  1. Congzhi Zhang (5 papers)
  2. Linhai Zhang (12 papers)
  3. Deyu Zhou (42 papers)
Citations (9)
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Summary

Exploring Front-door Adjustment for Multi-hop Fact Verification: The "Causal Walk" Approach

Introduction

The complexity of natural language often necessitates that assertions (claims) be verified against multiple pieces of evidence, drawing upon a multitude of sources. This integrative process, known as multi-hop fact verification, has presented unique challenges in natural language processing. A significant challenge arises from the tendency of models to learn spurious correlations within the training data, leading to biases that can significantly affect the model's performance on unbiased datasets. This paper introduces a novel approach, dubbed the "Causal Walk," which leverages causal inference through the front-door adjustment mechanism to address this issue. Specifically, the Causal Walk method aims to debias multi-hop fact verification by considering the claim-evidence graph's reasoning path on the query's true justification. This post seeks to elucidate the methodology, evaluation, and implications of this innovative approach.

Methodology

The essence of the Causal Walk method lies in its innovative use of causal inference principles to mitigate bias in multi-hop fact verification tasks. Given the complex nature of multi-hop fact verification, where a claim must be validated against a series of evidence pieces, identifying and incorporating causal paths becomes crucial. The proposed method introduces a structural causal model (SCM) that incorporates the reasoning path as a mediator variable to faithfully represent the causal relationship between the input (the claim-evidence graph), and the output (veracity of the claim). This model is sophisticated in that:

  • It Disentangles the Relationship between the claim-evidence graph and the veracity of the claim by employing a mediator (the reasoning path) that facilitates a clear causal inference.
  • It Employs Front-door Adjustment to calculate the causal effect of the claim-evidence graph on the veracity of the claim, capturing the true causality by decomposing it into two parts: the effect of the input on the mediator and the effect of the mediator on the output.

Evaluation

To assess the efficacy of the Causal Walk method, the researchers devised two unique datasets enriched with adversarial examples. The evaluation employed was meticulous, with the proposed method outperforming other debiasing techniques across the metrics.

Significance

The pioneering approach of utilizing a mediator variable (the reasoning path) to model the causality in multi-hop fact verification presents a significant leap forward. This methodology not only addresses bias more effectively but also enhances the interpretability of the verification process by elucidating the causal pathways involved. Moreover, by systematically quantifying the causal effects, the Causal Walk method paves the way for more robust and generalizable fact verification models.

Future Directions

The implications of this paper are profound, underscoring the potential for causal inference techniques to revolutionize multi-hop fact verification. Looking ahead, this approach beckons future research to dive deeper into causal modeling and explore its applicability across various datasets and domains. It also raises intriguing questions about combining causal inference with other advanced machine learning strategies to further refine the accuracy and reliability of fact verification models.

Conclusion

The Causal Walk method represents a novel and effective approach to addressing bias in multi-hop fact verification by leveraging causal inference through the front-door adjustment. By introducing a mediator variable to model the causal path between the input and output, this method not only debiases the verification process but also enhances its transparency and interpretability. It sets a new precedent in the field, suggesting promising directions for future research in applying causal inference principles to natural language processing tasks, thereby enabling the development of more reliable and generalizable fact verification models.

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