用图增强BERT和可训练插件，从金融对话中精准提取用户意图句子。
Fin-ExBERT: User Intent based Text Extraction in Financial Context using Graph-Augmented BERT and trainable Plugin
- 基于领域适配的BERT，结合低秩适配器实现小样本高效微调。
- 两阶段渐进解冻训练+动态阈值，提升抽取精度与鲁棒性。
- 适合金融客服对话分析、审计及问答系统，输出可解释且易部署。
金融对话转录文本因结构非正式、领域术语密集、意图分布不均，给句级信息抽取带来挑战。我们提出Fin-ExBERT，一种轻量级模块化框架，用于从标注的金融服务通话中提取用户意图相关句子。该方法基于领域适配的BERT（Bidirectional Encoder Representations from Transformers）主干，引入LoRA（Low-Rank Adaptation）适配器，实现有限标注数据下的高效微调。采用两阶段训练策略：先冻结主干训练分类头，再逐步解冻并使用差异化学习率微调全模型。为应对不确定性，提出基于概率曲率（肘部检测）的动态阈值策略，避免固定阈值启发式方法。实证结果在真实对话数据上表现优异，具备高精确率与F1值，输出可解释，适用于下游审计与问答流程。完整框架支持批量评估、可视化与校准导出，提供可部署的金融对话挖掘解决方案。
原文摘要 · Abstract (English)
Financial dialogue transcripts pose a unique challenge for sentence-level information extraction due to their informal structure, domain-specific vocabulary, and variable intent density. We introduce Fin-ExBERT, a lightweight and modular framework for extracting user intent-relevant sentences from annotated financial service calls. Our approach builds on a domain-adapted BERT (Bidirectional Encoder Representations from Transformers) backbone enhanced with LoRA (Low-Rank Adaptation) adapters, enabling efficient fine-tuning using limited labeled data. We propose a two-stage training strategy with progressive unfreezing: initially training a classifier head while freezing the backbone, followed by gradual fine-tuning of the entire model with differential learning rates. To ensure robust extraction under uncertainty, we adopt a dynamic thresholding strategy based on probability curvature (elbow detection), avoiding fixed cutoff heuristics. Empirical results show strong precision and F1 performance on real-world transcripts, with interpretable output suitable for downstream auditing and question-answering workflows. The full framework supports batched evaluation, visualization, and calibrated export, offering a deployable solution for financial dialogue mining.
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