Merging Two Grammar Worlds: Exploring the Relationship between Universal Dependencies and Signal Temporal Logic

ACL ARR 2025 July Submission1034 Authors

Abstract: Translating natural language requirements into Signal Temporal Logic (STL) is essential for safety-critical systems but requires mathematical expertise. We propose a translational grammar mapping Universal Dependencies (UD) structures to STL operators through twelve theoretically-motivated patterns, evaluated on 7,003 expert-annotated sentence-STL pairs. Our patterns achieve 97.8\% coverage in detecting temporal expressions, revealing systematic correspondences between syntactic structures and logical operators. Analysis uncovers rich syntactic variation (e.g., 70+ variants for conditionals) and probabilistic pattern-operator relationships, demonstrating that while our patterns reliably identify temporal expressions, their mapping to STL operators exhibits statistical tendencies rather than deterministic rules. These findings provide foundational insights for developing hybrid approaches that model uncertainty explicitly, combining syntactic patterns with semantic understanding to advance interpretable natural language interfaces for temporal logic specification.

Paper Type: Short

Research Area: Information Extraction

Research Area Keywords: Natural Language Processing, Temporal Logic, Universal Dependencies, Formal Specification Languages

Contribution Types: Model analysis & interpretability, Data analysis, Theory

Languages Studied: English

Reassignment Request Area Chair: This is not a resubmission

Reassignment Request Reviewers: This is not a resubmission

A1 Limitations Section: This paper has a limitations section.

A2 Potential Risks: No

A2 Elaboration: While the research focuses on translating natural language to Signal Temporal Logic for safety-critical systems (which could have significant real-world impacts), the paper does not discuss the risks associated on the ACL website.

B Use Or Create Scientific Artifacts: Yes

B1 Cite Creators Of Artifacts: Yes

B1 Elaboration: Section 3; A.3

B2 Discuss The License For Artifacts: No

B2 Elaboration: Dataset that was previously published for open use.

B3 Artifact Use Consistent With Intended Use: Yes

B3 Elaboration: Section 3; A.3

B4 Data Contains Personally Identifying Info Or Offensive Content: N/A

B4 Elaboration: N/A

B5 Documentation Of Artifacts: Yes

B5 Elaboration: Section 3; A.3

B6 Statistics For Data: Yes

B6 Elaboration: Section 4; A.4

C Computational Experiments: Yes

C1 Model Size And Budget: Yes

C1 Elaboration: Section 3; A.3

C2 Experimental Setup And Hyperparameters: Yes

C2 Elaboration: Section 3; A.3

C3 Descriptive Statistics: Yes

C3 Elaboration: Section 4; A.4

C4 Parameters For Packages: Yes

C4 Elaboration: Section 3; A.3

D Human Subjects Including Annotators: No

D1 Instructions Given To Participants: N/A

D1 Elaboration: N/A

D2 Recruitment And Payment: N/A

D2 Elaboration: N/A

D3 Data Consent: N/A

D3 Elaboration: N/A

D4 Ethics Review Board Approval: N/A

D4 Elaboration: N/A

D5 Characteristics Of Annotators: N/A

D5 Elaboration: N/A

E Ai Assistants In Research Or Writing: No

E1 Information About Use Of Ai Assistants: N/A

E1 Elaboration: N/A

Author Submission Checklist: yes

Submission Number: 1034