Papers by Wenxin Mao

2 papers
Visual-Linguistic Dependency Encoding for Image-Text Retrieval (2024.lrec-main)

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Challenge: Existing approaches to image-text retrieval ignore semantic discrepancies caused by syntactic structure in natural language expressions and relationships among visual entities.
Approach: They propose a visual-linguistic dependency encoder framework which explicitly models the dependency information among textual words and interaction patterns between image regions.
Outcome: The proposed framework outperforms existing methods on a vision-linguistic compositional structure reasoning dataset.
Enhancing Text-to-SQL Parsing through Question Rewriting and Execution-Guided Refinement (2024.findings-acl)

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Challenge: Existing prompt engineering methods exploit database content and execution feedback to improve text-to-sql performance.
Approach: They propose a framework for large language model-based text-to-sql task that exploits database content and execution feedback to improve execution accuracy.
Outcome: The proposed framework improves execution accuracy and usability by 12.41% and 5.38% on four widely used benchmarks.

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