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20222025
most citedExploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning

2 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2025

SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling

Xin Zhang, Qiyu Wei, Yingjie Zhu +3

User reviews on e-commerce platforms exhibit dynamic sentiment patterns driven by temporal and contextual factors. Traditional sentiment analysis methods focus on static reviews, f…

cs.CL2024★ 1 cited

CHECKWHY: Causal Fact Verification via Argument Structure

Jiasheng Si, Yibo Zhao, Yingjie Zhu +3

With the growing complexity of fact verification tasks, the concern with "thoughtful" reasoning capabilities is increasing. However, recent fact verification benchmarks mainly focu…

cs.CL2023

EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification

Yingjie Zhu, Jiasheng Si, Yibo Zhao +3

Automatic multi-hop fact verification task has gained significant attention in recent years. Despite impressive results, these well-designed models perform poorly on out-of-domain…

cs.CL2023

Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources

Jiasheng Si, Yingjie Zhu, Xingyu Shi +2

Given a controversial target such as ``nuclear energy'', argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring b…

cs.CL2023★ 2 cited

Consistent Multi-Granular Rationale Extraction for Explainable Multi-hop Fact Verification

Jiasheng Si, Yingjie Zhu, Deyu Zhou

The success of deep learning models on multi-hop fact verification has prompted researchers to understand the behavior behind their veracity. One possible way is erasure search: ob…

cs.CL2022★ 2 cited

Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning

Jiasheng Si, Yingjie Zhu, Deyu Zhou

The opaqueness of the multi-hop fact verification model imposes imperative requirements for explainability. One feasible way is to extract rationales, a subset of inputs, where the…