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[Submitted on 13 Sep 2026] Title:SignTrace: Describe a Sign, Find the Word View a PDF of the paper titled SignTrace: Describe a Sign, Find the Word, by Zengji Tu and 5 other authors View PDF HTML (experimental) Abstract:Identifying an unfamiliar sign is difficult when a learner remembers its movement but does not know its meaning or formal feature codes. SignTrace addresses this longstanding reverse-lookup problem through natural-language access to a Chinese sign-language dictionary. The system integrates LLM-based dictionary enrichment, action extraction, dictionary-style rewriting, seven-channel retrieval, and candidate reranking over 6,699 entries. It has been deployed for user trials and has received positive informal feedback. Evaluation on a dictionary-derived benchmark of 500 movement-description queries yields 94.0% Hit@1, 97.4% Hit@9, and a mean reciprocal rank of 0.9540. Reranking increases Hit@1 from 71.8% to 94.0%, while component analyses show the contribution of enriched entry descriptions. Median query-processing time is 13.37 seconds with six concurrent queries. By connecting everyday movement descriptions to documented signs and meanings, SignTrace provides a practical tool for identifying unfamiliar signs. Dictionary-derived wording and prior selection within the benchmark limit generalization to descriptions independently produced by users. Comments: Includes reproducibility data and method documentation Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) Cite as: arXiv:2609.30295 [cs.CL] (or arXiv:2609.30295v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.30295 arXiv-issued DOI via DataCite Submission history From: Zengji Tu [view email] [v1] Sun, 13 Sep 2026 14:44:14 UTC (203 KB) Full-text links: Access Paper: View a PDF of the paper titled SignTrace: Describe a Sign, Find the Word, by Zengji Tu and 5 other authors View PDF HTML (experimental) TeX Source view license Ancillary-file links: Ancillary files (details): README.md analyze.py api_summary.json computed_results.json dataset_identity.json example_records.json figure_labels_zh.json make_figures.py method_diagnostics.json methods/README.md methods/annotation_provenance.json methods/extraction_prompt.py methods/method_manifest.json methods/prompt_templates.json methods/rerank_prompt.py methods/retrieval_rules.py methods/rewrite_prompt.py questions500.json ranks500.csv recompute_metrics.py requirements.txt schema_manifest.json (17 additional files not shown) Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs cs.AI cs.IR References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)