@inproceedings{liu-etal-2026-beyond-bag,
title = "Beyond Bag-of-Words: Generative Structural Regularization for Dense Table Retrieval",
author = "Liu, WeiJie and
Chen, Shanshan and
Kong, Fang",
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.1275/",
pages = "29079--29094",
ISBN = "979-8-89176-459-0",
abstract = "Dense retrieval has become the dominant paradigm for large-scale table retrieval. However, existing contrastive retrievers often suffer from bag-of-words degradation: they rely on lexical overlap rather than row{--}column bindings. To quantify and mitigate this, we propose an adversarial benchmark and a structure-aware retrieval framework. Our benchmark uses ``evidence cell'' anchoring and a Homologous Structural Swapping mechanism to construct lexically equivalent but structurally invalid negative samples. Our model uses text generation as a structural regularizer for dense table representations. It decouples row and column anchors through orthogonal attention pooling, and employs dynamic masking to encourage autoregressive reconstruction. This process encourages the retrieval representations to retain table content and structure-related cues. Extensive experiments on OTT-QA, NQ-Tables, and our adversarial benchmark demonstrate the effectiveness of our method. It achieves competitive performance on conventional retrieval datasets and substantially improves structural robustness under adversarial settings."
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<abstract>Dense retrieval has become the dominant paradigm for large-scale table retrieval. However, existing contrastive retrievers often suffer from bag-of-words degradation: they rely on lexical overlap rather than row–column bindings. To quantify and mitigate this, we propose an adversarial benchmark and a structure-aware retrieval framework. Our benchmark uses “evidence cell” anchoring and a Homologous Structural Swapping mechanism to construct lexically equivalent but structurally invalid negative samples. Our model uses text generation as a structural regularizer for dense table representations. It decouples row and column anchors through orthogonal attention pooling, and employs dynamic masking to encourage autoregressive reconstruction. This process encourages the retrieval representations to retain table content and structure-related cues. Extensive experiments on OTT-QA, NQ-Tables, and our adversarial benchmark demonstrate the effectiveness of our method. It achieves competitive performance on conventional retrieval datasets and substantially improves structural robustness under adversarial settings.</abstract>
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%0 Conference Proceedings
%T Beyond Bag-of-Words: Generative Structural Regularization for Dense Table Retrieval
%A Liu, WeiJie
%A Chen, Shanshan
%A Kong, Fang
%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 liu-etal-2026-beyond-bag
%X Dense retrieval has become the dominant paradigm for large-scale table retrieval. However, existing contrastive retrievers often suffer from bag-of-words degradation: they rely on lexical overlap rather than row–column bindings. To quantify and mitigate this, we propose an adversarial benchmark and a structure-aware retrieval framework. Our benchmark uses “evidence cell” anchoring and a Homologous Structural Swapping mechanism to construct lexically equivalent but structurally invalid negative samples. Our model uses text generation as a structural regularizer for dense table representations. It decouples row and column anchors through orthogonal attention pooling, and employs dynamic masking to encourage autoregressive reconstruction. This process encourages the retrieval representations to retain table content and structure-related cues. Extensive experiments on OTT-QA, NQ-Tables, and our adversarial benchmark demonstrate the effectiveness of our method. It achieves competitive performance on conventional retrieval datasets and substantially improves structural robustness under adversarial settings.
%U https://aclanthology.org/2026.emnlp-main.1275/
%P 29079-29094
Markdown (Informal)
[Beyond Bag-of-Words: Generative Structural Regularization for Dense Table Retrieval](https://aclanthology.org/2026.emnlp-main.1275/) (Liu et al., EMNLP 2026)
ACL