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.
SignTrace: Describe a Sign, Find the Word
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
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example_records.json
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method_diagnostics.json
methods/README.md
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methods/rerank_prompt.py
methods/retrieval_rules.py
methods/rewrite_prompt.py
questions500.json
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requirements.txt
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