Pith. sign in

REVIEW 1 cited by

Generating Signed Language Instructions in Large-Scale Dialogue Systems

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2410.14026 v1 pith:NAHVZIZX submitted 2024-10-17 cs.CL cs.AIcs.CYcs.HC

classification cs.CLcs.AIcs.CYcs.HC
keywords systeminstructionsretrievallanguageaccessiblecommunityconversationalhttps
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We introduce a goal-oriented conversational AI system enhanced with American Sign Language (ASL) instructions, presenting the first implementation of such a system on a worldwide multimodal conversational AI platform. Accessible through a touch-based interface, our system receives input from users and seamlessly generates ASL instructions by leveraging retrieval methods and cognitively based gloss translations. Central to our design is a sign translation module powered by Large Language Models, alongside a token-based video retrieval system for delivering instructional content from recipes and wikiHow guides. Our development process is deeply rooted in a commitment to community engagement, incorporating insights from the Deaf and Hard-of-Hearing community, as well as experts in cognitive and ASL learning sciences. The effectiveness of our signing instructions is validated by user feedback, achieving ratings on par with those of the system in its non-signing variant. Additionally, our system demonstrates exceptional performance in retrieval accuracy and text-generation quality, measured by metrics such as BERTScore. We have made our codebase and datasets publicly accessible at https://github.com/Merterm/signed-dialogue, and a demo of our signed instruction video retrieval system is available at https://huggingface.co/spaces/merterm/signed-instructions.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Prompting with Sign Parameters for Low-resource Sign Language Instruction Generation

    cs.HC 2025-08 conditional novelty 6.0 of 10

    A new 60-word Bengali sign language instruction dataset and a sign-parameter-infused prompting method that modestly improves VLM-generated instructions on most text-matching metrics for larger models.

Pith tools