Fin-ExBERT：在金融语境中使用图增强 BERT 和可训练插件进行基于用户意图的文本抽取
Fin-ExBERT: User Intent based Text Extraction in Financial Context using Graph-Augmented BERT and trainable Plugin
AI总结：
针对金融对话记录的句子级意图信息抽取难题，提出模块化 Fin-ExBERT 框架，结合领域适配 BERT、LoRA、渐进式解冻训练与动态阈值，在真实数据上取得良好精确率和 F1。
摘要：
金融对话记录因其非正式结构、领域特定词汇和可变的意图密度，给句子级信息抽取带来了独特挑战。我们提出 Fin-ExBERT，这是一个轻量级、模块化框架，用于从已标注的金融服务电话中抽取与用户意图相关的句子。我们的方法建立在经过领域适配的 BERT（Bidirectional Encoder Representations from Transformers，来自 Transformer 的双向编码器表示）主干之上，并通过 LoRA（Low-Rank Adaptation，低秩适配）适配器加以增强，从而能够利用有限的标注数据进行高效微调。我们提出一种带有渐进式解冻的两阶段训练策略：首先在冻结主干的情况下训练分类器头，随后以差分学习率逐步微调整个模型。为确保在不确定性下实现稳健抽取，我们采用基于概率曲率（肘部检测）的动态阈值策略，避免固定截断启发式方法。实证结果表明，该方法在真实世界记录上具有出色的精确率和 F1 表现，并且输出具有可解释性，适合下游审计和问答工作流。完整框架支持批量评估、可视化和校准导出，为金融对话挖掘提供了可部署的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
