USR Builder: Tool for Automatic Generation of Universal Semantic Representation

ACL ARR 2025 July Submission684 Authors

Abstract: This paper presents an integrated software USR Builder that automatically creates the multilayered Universal Semantic Representation (USR) from Hindi texts. The proposed software applies a set of heuristics on the outputs of various NLP tools to produce the multilayered semantic representation, USR, for a given discourse. Since manual annotation of any text data is always a labor-intensive, time-consuming, error-prone and expensive task, it is never a feasible option to manually annotate a text from scratch. USR Builder provides an automated generation option for USRs. It generates USR automatically, which the annotators can validate and correct to obtain the final version. The tool evaluation scores validate the claim that this tool saves both time and effort compared to starting the manual annotation process from scratch and improves the quality of annotation by reducing the chances of manual error.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: multilayered semantic representation, automatic Universal Semantic Representation, NLP Tool

Contribution Types: Approaches to low-resource settings, Data resources, Data analysis

Languages Studied: Hindi

Reassignment Request Area Chair: This is not a resubmission

Reassignment Request Reviewers: This is not a resubmission

Justification For Not Keeping Action Editor Or Reviewers: NA

A1 Limitations Section: This paper has a limitations section.

A2 Potential Risks: N/A

B Use Or Create Scientific Artifacts: Yes

B1 Cite Creators Of Artifacts: Yes

B1 Elaboration: Section 3 Model Description

B2 Discuss The License For Artifacts: No

B2 Elaboration: Some artifacts are created in-house and rest are open source artifacts.

B3 Artifact Use Consistent With Intended Use: Yes

B3 Elaboration: Section 3

B4 Data Contains Personally Identifying Info Or Offensive Content: No

B4 Elaboration: Data has been taken from text books.

B5 Documentation Of Artifacts: N/A

B6 Statistics For Data: Yes

B6 Elaboration: Section 5.2.3

C Computational Experiments: Yes

C1 Model Size And Budget: N/A

C2 Experimental Setup And Hyperparameters: N/A

C3 Descriptive Statistics: Yes

C3 Elaboration: Section 6

C4 Parameters For Packages: N/A

D Human Subjects Including Annotators: Yes

D1 Instructions Given To Participants: N/A

D2 Recruitment And Payment: No

D2 Elaboration: Not relevant for this paper.

D3 Data Consent: No

D3 Elaboration: Not required.

D4 Ethics Review Board Approval: N/A

D5 Characteristics Of Annotators: No

D5 Elaboration: Not required.

E Ai Assistants In Research Or Writing: No

E1 Information About Use Of Ai Assistants: N/A

Author Submission Checklist: yes

Submission Number: 684