SignBank+: Preparing a Multilingual Sign Language Dataset for Machine Translation Using Large Language Models

Fuente: arXiv
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Main Authors: Moryossef, Amit, Jiang, Zifan
Format: Preprint
Published: 2023
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author Moryossef, Amit
Jiang, Zifan
author_facet Moryossef, Amit
Jiang, Zifan
contents We introduce SignBank+, a clean version of the SignBank dataset, optimized for machine translation between spoken language text and SignWriting, a phonetic sign language writing system. In addition to previous work that employs complex factorization techniques to enable translation between text and SignWriting, we show that a traditional text-to-text translation approach performs equally effectively on the cleaned SignBank+ dataset. Our evaluation results indicate that models trained on SignBank+ surpass those on the original dataset, establishing a new benchmark for SignWriting-based sign language translation and providing an open resource for future research.
format Preprint
id arxiv_https___arxiv_org_abs_2309_11566
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle SignBank+: Preparing a Multilingual Sign Language Dataset for Machine Translation Using Large Language Models
Moryossef, Amit
Jiang, Zifan
Computation and Language
We introduce SignBank+, a clean version of the SignBank dataset, optimized for machine translation between spoken language text and SignWriting, a phonetic sign language writing system. In addition to previous work that employs complex factorization techniques to enable translation between text and SignWriting, we show that a traditional text-to-text translation approach performs equally effectively on the cleaned SignBank+ dataset. Our evaluation results indicate that models trained on SignBank+ surpass those on the original dataset, establishing a new benchmark for SignWriting-based sign language translation and providing an open resource for future research.
title SignBank+: Preparing a Multilingual Sign Language Dataset for Machine Translation Using Large Language Models
topic Computation and Language
url https://arxiv.org/abs/2309.11566