Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval

ACL ARR 2024 December Submission1775 Authors

Abstract: Document retrieval in real-world scenarios faces significant challenges due to diverse document formats and modalities. Traditional text-based approaches rely on tailored parsing techniques that disregard layout information and are prone to errors, while recent parsing-free visual methods often struggle to capture fine-grained textual semantics in text-rich scenarios. To address these limitations, we propose Unveil, a novel visual-textual embedding framework that effectively integrates textual and visual features for robust document representation. Through knowledge distillation, we transfer the semantic understanding capabilities from the visual-textual embedding model to a purely visual model, enabling efficient parsing-free retrieval while preserving semantic fidelity. Experimental results demonstrate that our visual-textual embedding method surpasses existing approaches, while knowledge distillation successfully bridges the performance gap between visual-textual and visual-only methods, improving both retrieval accuracy and efficiency.

Paper Type: Long

Research Area: Information Retrieval and Text Mining

Research Area Keywords: Multi-modal Document Retrieval, Vision Language Model

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 1775