Papers by Lianwei Wu
DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (2020.acl-main)
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| Challenge: | Recent methods to discover evidence for explainable claim verification are nontransparent and unexplained. |
| Approach: | They propose a Decision Tree-based Co-Attention model to discover evidence for explainable claim verification using neural networks. |
| Outcome: | The proposed model boosts the F1-score by more than 3.11%, 2.41% on two public datasets. |
Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models (2024.lrec-main)
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| Challenge: | Existing methods for autoregressive text generation have low controllability and accumulating errors. |
| Approach: | They propose a three-stage prompt-based approach to express autoregressive text in a specific region editing task using a word frequency-based strategy. |
| Outcome: | Experiments on publicly competitive datasets confirm that the proposed approach achieves state-of-the-art performance. |
Cultivating Forensic Reasoning for Generalizable Multimodal Manipulation Detection (2026.acl-long)
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| Challenge: | Existing methods for manipulation detection and grounding focus on manipulator type classification under result-oriented supervision. |
| Approach: | They propose a reasoning-driven framework that shifts learning from outcome fitting to process modeling. |
| Outcome: | The proposed framework achieves state-of-the-art with superior generalization on large-scale datasets. |
From Form to Logic: Masked Reconstruction and Reasoning Distillation for Short Video Fake News Detection (2026.acl-long)
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| Challenge: | Existing detectors that detect short video fake news suffer from global-alignment bias and lack generative reasoning are too late. |
| Approach: | They propose a Perception-Cognition Dual-driven Detector that jointly observes the form and probes the logic for short video fake news detection. |
| Outcome: | The proposed detector outperforms baseline detectors on real-world datasets while improving interpretability and robustness in data scarcity scenarios. |
Zero-shot Cross-lingual Conversational Semantic Role Labeling (2022.findings-naacl)
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| Challenge: | Xu et al., 2021: conversational semantic role labeling is under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training. |
| Approach: | They propose a model that implicitly learns conversational structure-aware representations with hierarchical encoders and elaborately designed pre-training objectives. |
| Outcome: | The proposed model outperforms baselines on English CSRL tests by large margins . it will facilitate the research of non-Chinese dialogue tasks which suffer from ellipsis and anaphora . |
Unified Dual-view Cognitive Model for Interpretable Claim Verification (2021.acl-long)
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| Challenge: | Existing studies constructing direct interactions between the claim and each single user response to capture evidence have shown remarkable success in interpretable claim verification. |
| Approach: | They propose a Dual-view model based on the views of Collective and Individual Cognition (CICD) that captures word-level semantics based . on individual cognition, they adjust the proportion between them to generate global evidence. |
| Outcome: | The proposed model is based on the views of collective and individual cognition and achieves state-of-the-art performance on three benchmark datasets. |
Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline (2026.acl-long)
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| Challenge: | Existing facial forgery detection methods focus on binary classification or pixel-level localization, providing little semantic insight into the nature of the manipulation. |
| Approach: | They propose a multimodal task that localizes forged regions and generates natural language explanations grounded in editing process. |
| Outcome: | The proposed task localizes forged regions and generates natural language explanations grounded in editing process. |
STRIDE-ED: A Strategy-Grounded Stepwise Reasoning Framework for Empathetic Dialogue Systems (2026.acl-long)
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| Challenge: | Empathetic dialogue requires not only recognizing a user’s emotional state but also making strategy-aware, context-sensitive decisions throughout response generation. |
| Approach: | They propose a STRategy-grounded, interpretable, and DEep reasoning framework that models Empathetic Dialogue through structured, strategy-conditioned reasoning. |
| Outcome: | The proposed framework outperforms existing methods on automatic metrics and human evaluations. |
Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection (D19-1)
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| Challenge: | Existing methods for detecting fake news use shared features as complementarity features without selection. |
| Approach: | They propose a sifted multi-task learning method with a selected sharing layer for fake news detection. |
| Outcome: | The proposed method boosts the F1-score by more than 0.87%, 1.31% on two public and widely used competition datasets. |