# A Semantic Parsing Framework for End-to-End Time Normalization

arXiv: https://arxiv.org/abs/2507.06450 · HTML: https://mlsanity.com/paper/2507.06450/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning](https://mlsanity.com/paper/2305.13971/) (2023)
- [CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://mlsanity.com/paper/2305.05711/) (2023)
- [Retrieval-Augmented Code Generation for Universal Information Extraction](https://mlsanity.com/paper/2311.02962/) (2023)
- [KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction](https://mlsanity.com/paper/2403.07969/) (2024)
- [Multilingual Normalization of Temporal Expressions with Masked Language Models](https://mlsanity.com/paper/2205.10399/) (2022)
- [What time is it? Temporal Analysis of Novels](https://mlsanity.com/paper/2011.04124/) (2020)
- [Doc2Dict: Information Extraction as Text Generation](https://mlsanity.com/paper/2105.07510/) (2021)
- [Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering](https://mlsanity.com/paper/2310.19292/) (2023)
- [KnowCoder-X: Boosting Multilingual Information Extraction via Code](https://mlsanity.com/paper/2411.04794/) (2024)

## Built on by (most cited first, 1 of 1)

- [ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training](https://mlsanity.com/paper/2609.00188/) (2026)

## Abstract (arXiv)

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.