Papers by Xiaowang Zhang
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension (2022.acl-long)
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| Challenge: | Existing methods to zero-shot transfer knowledge from rich-resource to low-resourced languages are limited due to linguistic discrepancies in different languages. |
| Approach: | They propose a multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model to disassociate semantics from syntax in models learned by multilingual pre-trained models. |
| Outcome: | The proposed model disassociates semantics from syntax in multilingual models. |
Mitigating Language Bias of LMMs in Social Intelligence Understanding with Virtual Counterfactual Calibration (2024.emnlp-main)
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| Challenge: | Existing work on social intelligence using large multimodal models is under-explored due to the prevalence of text-based data in the pretraining stage. |
| Approach: | They propose a structure causal model to mitigate the negative language biases of large multimodal models by preserving beneficial priors. |
| Outcome: | The proposed model minimizes negative language bias while preserving beneficial priors while avoiding spurious correlations between LMMs' internal commonsense knowledge and the given context. |
A Reinforcement Learning Framework for Cross-Lingual Stance Detection Using Chain-of-Thought Alignment (2025.findings-acl)
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| Challenge: | Existing approaches to cross-lingual stance detection can't effectively perform cross-linguistic transfer of complex reasoning processes. |
| Approach: | They propose a framework to facilitate cross-lingual transfer of complex reasoning processes in stance detection by using cross-linguistic Chain-of-Thought alignment to obtain high-quality CoTs generated from target language inputs. |
| Outcome: | The proposed framework outperforms competing models on four multilingual datasets. |
Faithful Inference Chains Extraction for Fact Verification over Multi-view Heterogeneous Graph with Causal Intervention (2025.coling-main)
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| Challenge: | Existing methods for fact verification do not extract faithful inference chains due to the diversity of relation paths. |
| Approach: | They propose a multi-view heterogeneous Graph with causal intervention to extract evidence graphs from the knowledge graph. |
| Outcome: | The proposed model provides precise evidence graphs and achieves state-of-the-art performance on the public KG-based fact verification dataset FactKG. |
Document-level Relationship Extraction by Bidirectional Constraints of Beta Rules (2023.emnlp-main)
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| Challenge: | Document-level Relation Extraction (DocRE) aims to extract relations among entity pairs in documents. |
| Approach: | They propose a logic constraint framework that uses bidirectional constraints to model rules by beta contribtion and reconstruct rule consistency loss by bidirectional constraint. |
| Outcome: | The proposed framework outperforms existing models in relation extraction performance and logical consistency. |
Causal Intervention for Mitigating Name Bias in Machine Reading Comprehension (2023.findings-acl)
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| Challenge: | Existing MRC models may overuse name information to make predictions, causing name bias . |
| Approach: | They propose a Causal Interventional paradigm for MRC to mitigate name bias by analyzing pre-trained knowledge and context representations. |
| Outcome: | The proposed model is robust to names and performs competitively on the original SQuAD. |
Re-embedding Difficult Samples via Mutual Information Constrained Semantically Oversampling for Imbalanced Text Classification (2021.emnlp-main)
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| Challenge: | Existing frameworks for imbalanced text classification can generate anchor instances for difficult samples . difficult samples are hard to classify as they are embedded into an overlapping semantic region with the majority class. |
| Approach: | They propose a Mutual Information constrained Semantically Oversampling framework that generates anchor instances for difficult samples to help the backbone network determine the re-embedding position of a non-overlapping representation. |
| Outcome: | The proposed framework can generate anchor instances to help classifiers achieve significant improvements over baselines on a variety of imbalanced text classification tasks. |
An Event-based Abductive Learning for Hard Time-sensitive Question Answering (2024.lrec-main)
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| Challenge: | Existing time-sensitive question answering models are limited for hard time-sensitive questions whose time qualifiers are implicit in the document. |
| Approach: | They propose a time-sensitive question answering framework that matches temporal events in documents with time qualifiers. |
| Outcome: | The proposed model outperforms baseline models for hard time-sensitive questions with 12.7% improvement in EM scores. |
Flow-Based Page Unique Semantic Mapping Architecture for Document Visual Question Answering (2026.acl-long)
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| Challenge: | Document Visual Question Answering (DocVQA) aims to generate answers by understanding textual, layout, and visual elements within document images. |
| Approach: | They propose a Flow-Based Page Unique Semantic Mapping Architecture to solve the distinguishability problem among semantically similar pages. |
| Outcome: | The proposed model outperforms existing methods in evidence localization and answer generation. |