改进模型从无结构金融文档中精准提取关键信息
ViBERTgrid BiLSTM-CRF: Multimodal Key Information Extraction from Unstructured Financial Documents
- 在ViBERTgrid基础上加入BiLSTM-CRF层处理无结构文本
- 金融领域命名实体识别准确率提升最高达2个百分点
- 公开了SROIE数据集的细粒度标注，利于后续研究
多模态关键信息提取（KIE）模型在半结构化文档上已有广泛研究，但在无结构文档上的探索仍属新兴方向。本文提出将先前用于半结构化文档的多模态Transformer模型ViBERTgrid适配至无结构金融文档，通过引入BiLSTM-CRF层实现改进。所提出的ViBERTgrid BiLSTM-CRF模型在金融领域无结构文档的命名实体识别任务上表现显著提升，性能最高提升2个百分点，同时保持了在半结构化文档上的KIE性能。此外，本文公开发布了SROIE数据集的逐标记级标注，以促进多模态序列标注模型的研究与应用。
原文摘要 · Abstract (English)
Multimodal key information extraction (KIE) models have been studied extensively on semi-structured documents. However, their investigation on unstructured documents is an emerging research topic. The paper presents an approach to adapt a multimodal transformer (i.e., ViBERTgrid previously explored on semi-structured documents) for unstructured financial documents, by incorporating a BiLSTM-CRF layer. The proposed ViBERTgrid BiLSTM-CRF model demonstrates a significant improvement in performance (up to 2 percentage points) on named entity recognition from unstructured documents in financial domain, while maintaining its KIE performance on semi-structured documents. As an additional contribution, we publicly released token-level annotations for the SROIE dataset in order to pave the way for its use in multimodal sequence labeling models.
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