# DeepSTL -- From English Requirements to Signal Temporal Logic

arXiv: https://arxiv.org/abs/2109.10294 · HTML: https://mlsanity.com/paper/2109.10294/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 59 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [Reinforcement Learning With Temporal Logic Rewards](https://mlsanity.com/paper/1612.03471/) (2016)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [Dive into Deep Learning](https://mlsanity.com/paper/2106.11342/) (2021)
- [NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System](https://mlsanity.com/paper/1802.08979/) (2018)
- [Q-Learning for Robust Satisfaction of Signal Temporal Logic Specifications](https://mlsanity.com/paper/1609.07409/) (2016)
- [Model-based Reinforcement Learning from Signal Temporal Logic Specifications](https://mlsanity.com/paper/2011.04950/) (2020)
- [Formal Consistency Checking over Specifications in Natural Languages](https://mlsanity.com/paper/1405.5003/) (2014)
- [Distributed Communication-aware Motion Planning for Multi-agent Systems from STL and SpaTeL Specifications](https://mlsanity.com/paper/1705.10259/) (2017)
- [ARSENAL: Automatic Requirements Specification Extraction from Natural Language](https://mlsanity.com/paper/1403.3142/) (2014)

## Built on by (most cited first, 22 of 22)

- [AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers](https://mlsanity.com/paper/2306.06531/) (2023)
- [nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Models](https://mlsanity.com/paper/2303.04864/) (2023)
- [NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models](https://mlsanity.com/paper/2305.07766/) (2023)
- [Formal Specifications from Natural Language](https://mlsanity.com/paper/2206.01962/) (2022)
- [TR2MTL: LLM based framework for Metric Temporal Logic Formalization of Traffic Rules](https://mlsanity.com/paper/2406.05709/) (2024)
- [TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching](https://mlsanity.com/paper/2505.00562/) (2025)
- [Enhancing Transformation from Natural Language to Signal Temporal Logic Using LLMs with Diverse External Knowledge](https://mlsanity.com/paper/2505.20658/) (2025)
- [STLCG++: A Masking Approach for Differentiable Signal Temporal Logic Specification](https://mlsanity.com/paper/2501.04194/) (2025)
- [Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications](https://mlsanity.com/paper/2306.06808/) (2023)
- [Verifiable Natural Language to Linear Temporal Logic Translation: A Benchmark Dataset and Evaluation Suite](https://mlsanity.com/paper/2507.00877/) (2025)
- [stl2vec: Semantic and Interpretable Vector Representation of Temporal Logic](https://mlsanity.com/paper/2405.14389/) (2024)
- [Application of AI to formal methods - an analysis of current trends](https://mlsanity.com/paper/2411.14870/) (2024)
- [ClarifySTL: An Interactive LLM Agent Framework for STL Transformation through Requirements Clarification](https://mlsanity.com/paper/2605.01209/) (2026)
- [STeP: Signal Temporal Logic for Precise Specifications for Action Generation with Vision Language Models](https://mlsanity.com/paper/2607.18580/) (2026)
- [Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers](https://mlsanity.com/paper/2507.07808/) (2025)
- [Structural-Ambiguity-Aware Translation from Natural Language to Signal Temporal Logic](https://mlsanity.com/paper/2603.28426/) (2026)
- [ReasonSTL: Bridging Natural Language and Signal Temporal Logic via Tool-Augmented Process-Rewarded Learning](https://mlsanity.com/paper/2605.06483/) (2026)
- [SCP-NL2TL: Selective Conformal Prediction with Semantic Verification for Natural Language to Temporal Logic Specifications](https://mlsanity.com/paper/2608.05439/) (2026)
- [Handling Open-Vocabulary Constructs in Formalizing Specifications: Retrieval-Augmented Parsing with Expert Knowledge](https://mlsanity.com/paper/2509.08808/) (2025)
- [LTLGuard: Formalizing LTL Specifications with Compact Language Models and Lightweight Symbolic Reasoning](https://mlsanity.com/paper/2603.05728/) (2026)
- [Translating Natural Language to Strategic Temporal Specifications via LLMs](https://mlsanity.com/paper/2606.30441/) (2026)
- [Feedback Makes Perfect: A Closed-Loop Framework for NL-to-STL Translation](https://mlsanity.com/paper/2609.33287/) (2026)

## Abstract (arXiv)

Formal methods provide very powerful tools and techniques for the design and analysis of complex systems. Their practical application remains however limited, due to the widely accepted belief that formal methods require extensive expertise and a steep learning curve. Writing correct formal specifications in form of logical formulas is still considered to be a difficult and error prone task. In this paper we propose DeepSTL, a tool and technique for the translation of informal requirements, given as free English sentences, into Signal Temporal Logic (STL), a formal specification language for cyber-physical systems, used both by academia and advanced research labs in industry. A major challenge to devise such a translator is the lack of publicly available informal requirements and formal specifications. We propose a two-step workflow to address this challenge. We first design a grammar-based generation technique of synthetic data, where each output is a random STL formula and its associated set of possible English translations. In the second step, we use a state-of-the-art transformer-based neural translation technique, to train an accurate attentional translator of English to STL. The experimental results show high translation quality for patterns of English requirements that have been well trained, making this workflow promising to be extended for processing more complex translation tasks.