Sari la conținut

Română · beta. Traducerea a fost făcută automat și este încă în curs de revizuire. Citește pagina în engleză

perspectiva aiqbenchProduse AI

analiza aiqbench

SignTrace leverages large language models to solve the reverse-lookup problem in sign language, allowing users to identify signs based on natural movement descriptions rather than formal codes. The system achieves high retrieval accuracy by integrating LLM-based enrichment and reranking techniques over a substantial dictionary of entries. This work demonstrates a practical application of generative AI for accessibility in specialized domains like sign language interpretation.
Sursă care acoperăarXiv
Ce este aceastaLectura unui model asupra articolului și a acoperirii conexe, scrisă pentru aiqbench. Nu sunt cuvintele publicației.

SignTrace: Describe a Sign, Find the Word

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.30295v1 Announce Type: new 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.

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.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)

arXiv:2609.30295 [cs.CL]

(or arXiv:2609.30295v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.30295 Focus to learn more arXiv-issued DOI via DataCite

api_summary.json

computed_results.json

dataset_identity.json

example_records.json

figure_labels_zh.json

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

recompute_metrics.py

requirements.txt

schema_manifest.json

(17 additional files not shown) You must enabled JavaScript to view entire file list.

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.

Text integral sub licența deschisă a publicației · 281 cuvinte

Subiecte conexe