# DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification

arXiv: https://arxiv.org/abs/2004.13455 · HTML: https://mlsanity.com/paper/2004.13455/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 17 of 17)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [ProoFVer: Natural Logic Theorem Proving for Fact Verification](https://mlsanity.com/paper/2108.11357/) (2021)
- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing](https://mlsanity.com/paper/2112.06924/) (2021)
- [Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning](https://mlsanity.com/paper/2212.01060/) (2022)
- [Automated Justification Production for Claim Veracity in Fact Checking: A Survey on Architectures and Approaches](https://mlsanity.com/paper/2407.12853/) (2024)
- [Unified Dual-view Cognitive Model for Interpretable Claim Verification](https://mlsanity.com/paper/2105.09567/) (2021)
- [Face the Facts! Evaluating RAG-based Pipelines for Professional Fact-Checking](https://mlsanity.com/paper/2412.15189/) (2024)
- [ExClaim: Explainable Neural Claim Verification Using Rationalization](https://mlsanity.com/paper/2301.08914/) (2023)
- [Consistent Multi-Granular Rationale Extraction for Explainable Multi-hop Fact Verification](https://mlsanity.com/paper/2305.09400/) (2023)
- [Evaluating BERT-based Pre-training Language Models for Detecting Misinformation](https://mlsanity.com/paper/2203.07731/) (2022)
- [Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Trees](https://mlsanity.com/paper/2304.13895/) (2023)
- [R-ODE: Ricci Curvature Tells When You Will be Informed](https://mlsanity.com/paper/2405.17282/) (2024)
- [Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection](https://mlsanity.com/paper/2404.16076/) (2024)
- [RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict](https://mlsanity.com/paper/2403.16662/) (2024)
- [Out-of-distribution Rumor Detection via Test-Time Adaptation](https://mlsanity.com/paper/2403.17735/) (2024)

## Abstract (arXiv)

Recently, many methods discover effective evidence from reliable sources by appropriate neural networks for explainable claim verification, which has been widely recognized. However, in these methods, the discovery process of evidence is nontransparent and unexplained. Simultaneously, the discovered evidence only roughly aims at the interpretability of the whole sequence of claims but insufficient to focus on the false parts of claims. In this paper, we propose a Decision Tree-based Co-Attention model (DTCA) to discover evidence for explainable claim verification. Specifically, we first construct Decision Tree-based Evidence model (DTE) to select comments with high credibility as evidence in a transparent and interpretable way. Then we design Co-attention Self-attention networks (CaSa) to make the selected evidence interact with claims, which is for 1) training DTE to determine the optimal decision thresholds and obtain more powerful evidence; and 2) utilizing the evidence to find the false parts in the claim. Experiments on two public datasets, RumourEval and PHEME, demonstrate that DTCA not only provides explanations for the results of claim verification but also achieves the state-of-the-art performance, boosting the F1-score by 3.11%, 2.41%, respectively.