@inproceedings{dong-etal-2026-utilize,
title = "How to Utilize Complementary Vision-Text Information for 2{D} Structure Understanding",
author = "Dong, Jiancheng and
Jia, Pengyue and
Xu, Derong and
Cheng, Jiawei and
Peng, Jingyu and
Zhang, Chao and
Liu, Bowen and
Sun, Xin and
Su, Lixin and
Wang, Shuaiqiang and
Yin, Dawei and
Zhao, Xiangyu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2081/",
pages = "47398--47416",
ISBN = "979-8-89176-459-0",
abstract = "LLMs typically linearize 2D tables into 1D sequences to fit their autoregressive architecture, which weakens row-column adjacency and other layout cues. In contrast, purely visual encoders can capture spatial cues, yet often struggle to preserve exact cell text. Our analysis reveals that these two modalities provide highly distinct information to LLMs and exhibit strong complementarity. However, direct concatenation and other fusion methods yield limited gains and frequently introduce cross-modal interference. To address this issue, we propose DiVA-Former, a lightweight architecture designed to effectively integrate vision and text information. DiVA-Former leverages visual tokens as dynamic queries to distill long textual sequences into digest vectors, thereby effectively exploiting complementary vision-text information. Evaluated across 13 table benchmarks, DiVA-Former improves upon the pure-text baseline by 23.9{\%} and achieves consistent gains over existing baselines using visual inputs, textual inputs, or a combination of both. Our code is available at: https://github.com/Applied-Machine-Learning-Lab/EMNLP26{\_}DiVA"
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<abstract>LLMs typically linearize 2D tables into 1D sequences to fit their autoregressive architecture, which weakens row-column adjacency and other layout cues. In contrast, purely visual encoders can capture spatial cues, yet often struggle to preserve exact cell text. Our analysis reveals that these two modalities provide highly distinct information to LLMs and exhibit strong complementarity. However, direct concatenation and other fusion methods yield limited gains and frequently introduce cross-modal interference. To address this issue, we propose DiVA-Former, a lightweight architecture designed to effectively integrate vision and text information. DiVA-Former leverages visual tokens as dynamic queries to distill long textual sequences into digest vectors, thereby effectively exploiting complementary vision-text information. Evaluated across 13 table benchmarks, DiVA-Former improves upon the pure-text baseline by 23.9% and achieves consistent gains over existing baselines using visual inputs, textual inputs, or a combination of both. Our code is available at: https://github.com/Applied-Machine-Learning-Lab/EMNLP26_DiVA</abstract>
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%0 Conference Proceedings
%T How to Utilize Complementary Vision-Text Information for 2D Structure Understanding
%A Dong, Jiancheng
%A Jia, Pengyue
%A Xu, Derong
%A Cheng, Jiawei
%A Peng, Jingyu
%A Zhang, Chao
%A Liu, Bowen
%A Sun, Xin
%A Su, Lixin
%A Wang, Shuaiqiang
%A Yin, Dawei
%A Zhao, Xiangyu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F dong-etal-2026-utilize
%X LLMs typically linearize 2D tables into 1D sequences to fit their autoregressive architecture, which weakens row-column adjacency and other layout cues. In contrast, purely visual encoders can capture spatial cues, yet often struggle to preserve exact cell text. Our analysis reveals that these two modalities provide highly distinct information to LLMs and exhibit strong complementarity. However, direct concatenation and other fusion methods yield limited gains and frequently introduce cross-modal interference. To address this issue, we propose DiVA-Former, a lightweight architecture designed to effectively integrate vision and text information. DiVA-Former leverages visual tokens as dynamic queries to distill long textual sequences into digest vectors, thereby effectively exploiting complementary vision-text information. Evaluated across 13 table benchmarks, DiVA-Former improves upon the pure-text baseline by 23.9% and achieves consistent gains over existing baselines using visual inputs, textual inputs, or a combination of both. Our code is available at: https://github.com/Applied-Machine-Learning-Lab/EMNLP26_DiVA
%U https://aclanthology.org/2026.emnlp-main.2081/
%P 47398-47416
Markdown (Informal)
[How to Utilize Complementary Vision-Text Information for 2D Structure Understanding](https://aclanthology.org/2026.emnlp-main.2081/) (Dong et al., EMNLP 2026)
ACL
- Jiancheng Dong, Pengyue Jia, Derong Xu, Jiawei Cheng, Jingyu Peng, Chao Zhang, Bowen Liu, Xin Sun, Lixin Su, Shuaiqiang Wang, Dawei Yin, and Xiangyu Zhao. 2026. How to Utilize Complementary Vision-Text Information for 2D Structure Understanding. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 47398–47416, Budapest, Hungary. Association for Computational Linguistics.