Papers with semantic interference

3 papers
Thesis Proposal: Diagnosing and Mitigating Semantic Interference in Script-Sharing Low-Resource Language Models: A Case Study on Square Bai Script (2026.acl-srw)

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Challenge: Multilingual models cover more languages than ever, yet script-sharing low-resource languages remain vulnerable to failures driven by script and dominant-language priors.
Approach: They propose to diagnose semantic interference in Square Bai Script, a script-sharing low-resource language . they will compare adaptation strategies under realistic compute constraints and estimate when shared-script transfer helps or harms adaptation.
Outcome: The proposed model underperforms in a script-sharing low-resource setting because Bai is low-source and because it treats visible overlap as safe transfer by default.
Memory-enhanced Large Language Model for Cross-lingual Dependency Parsing via Deep Hierarchical Syntax Understanding (2025.findings-emnlp)

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Challenge: Experimental results show that our approach can significantly improve the parsing accuracy of all baseline models, leading to new state-of-the-art results.
Approach: They propose a deep hierarchical syntax understanding approach to improve the cross-lingual semantic memory capability of large language models by implicitly aligning linguistic knowledge between source and target languages.
Outcome: The proposed approach improves the cross-lingual semantic memory capability of large language models by combining implicit multi-task fine-tuning and explicit label bank guiding.
DEBAR: Mitigating Contextual Bias in Cross-Document Relation Extraction via Dual-Stream Decoupling (2026.acl-long)

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Challenge: Existing methods focus on sentence-level or singledocument settings, resulting in one-sided relation transfer contextual bias and incomplete reasoning chains.
Approach: They propose a framework to explicitly decouple and preserve bidirectional bridge evidence and a dynamic loss optimization objective to separate head and tail contexts.
Outcome: The proposed framework decouples and preserves bidirectional bridge evidence while capturing global dependencies through iterative message passing.

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