@inproceedings{cui-etal-2023-pv2tea,
    title = "{PV}2{TEA}: Patching Visual Modality to Textual-Established Information Extraction",
    author = "Cui, Hejie  and
      Lin, Rongmei  and
      Zalmout, Nasser  and
      Zhang, Chenwei  and
      Shang, Jingbo  and
      Yang, Carl  and
      Li, Xian",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.127",
    doi = "10.18653/v1/2023.findings-acl.127",
    pages = "2026--2041",
    abstract = "Information extraction, e.g., attribute value extraction, has been extensively studied and formulated based only on text. However, many attributes can benefit from image-based extraction, like color, shape, pattern, among others. The visual modality has long been underutilized, mainly due to multimodal annotation difficulty. In this paper, we aim to patch the visual modality to the textual-established attribute in- formation extractor. The cross-modality integration faces several unique challenges: (C1) images and textual descriptions are loosely paired intra-sample and inter-samples; (C2) images usually contain rich backgrounds that can mislead the prediction; (C3) weakly supervised labels from textual-established ex- tractors are biased for multimodal training. We present PV2TEA, an encoder-decoder architecture equipped with three bias reduction schemes: (S1) Augmented label-smoothed contrast to improve the cross-modality alignment for loosely-paired image and text; (S2) Attention-pruning that adaptively distinguishes the visual foreground; (S3) Two-level neighborhood regularization that mitigates the label textual bias via reliability estimation. Empirical results on real-world e-Commerce datasets1 demonstrate up to 11.74{\%} absolute (20.97{\%} relatively) F1 increase over unimodal baselines.",
}

Information extraction, e.g., attribute value extraction, has been extensively studied and formulated based only on text. However, many attributes can benefit from image-based extraction, like color, shape, pattern, among others. The visual modality has long been underutilized, mainly due to multimodal annotation difficulty. In this paper, we aim to patch the visual modality to the textual-established attribute in- formation extractor. The cross-modality integration faces several unique challenges: (C1) images and textual descriptions are loosely paired intra-sample and inter-samples; (C2) images usually contain rich backgrounds that can mislead the prediction; (C3) weakly supervised labels from textual-established ex- tractors are biased for multimodal training. We present PV2TEA, an encoder-decoder architecture equipped with three bias reduction schemes: (S1) Augmented label-smoothed contrast to improve the cross-modality alignment for loosely-paired image and text; (S2) Attention-pruning that adaptively distinguishes the visual foreground; (S3) Two-level neighborhood regularization that mitigates the label textual bias via reliability estimation. Empirical results on real-world e-Commerce datasets1 demonstrate up to 11.74{\%} absolute (20.97{\%} relatively) F1 increase over unimodal baselines.
Cui, Hejie Lin, Rongmei Zalmout, Nasser Zhang, Chenwei Shang, Jingbo Yang, Carl Li, Xian
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction