用代码生成方式实现精准时间归一化，让模型自动理解复杂时间表达。
A Semantic Parsing Framework for End-to-End Time Normalization
- 将时间归一化视为代码生成任务，用符号化操作符构建语义表达
- 小模型在自动生成数据上训练后性能超越大模型父本
- 适合需要可解释性与本地部署的医疗、问答系统场景
时间归一化是将自然语言时间表达转换为机器可读表示的任务，支撑信息检索、问答系统和临床决策等应用。传统基于 ISO-TimeML 模式的系统表达能力有限，难以处理复合、事件相关及多跨度时间表达。本文提出一种基于 SCATE 框架的时间归一化新范式，通过符号化与组合式操作符定义时间语义。我们实现了一个可执行的 SCATE Python 库，并证明大语言模型（LLMs）可生成可执行的 SCATE 代码。利用此能力，我们构建了基于 LLM 的自动数据增强流水线，合成大规模带代码级验证的标注数据。实验表明，使用该数据训练的小型本地模型能实现优异性能，甚至超过其 LLM 父模型，实现高效、准确且可解释的时间归一化。
原文摘要 · Abstract (English)
Time normalization is the task of converting natural language temporal expressions into machine-readable representations. It underpins many downstream applications in information retrieval, question answering, and clinical decision-making. Traditional systems based on the ISO-TimeML schema limit expressivity and struggle with complex constructs such as compositional, event-relative, and multi-span time expressions. In this work, we introduce a novel formulation of time normalization as a code generation task grounded in the SCATE framework, which defines temporal semantics through symbolic and compositional operators. We implement a fully executable SCATE Python library and demonstrate that large language models (LLMs) can generate executable SCATE code. Leveraging this capability, we develop an automatic data augmentation pipeline using LLMs to synthesize large-scale annotated data with code-level validation. Our experiments show that small, locally deployable models trained on this augmented data can achieve strong performance, outperforming even their LLM parents and enabling practical, accurate, and interpretable time normalization.
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