Development of Natural Language Processing based Communication and Educational
            Assisted Systems for the People with Hearing Disability in Myanmar

Moe, Swe Zin; Thu, Ye Kyaw; Nwe, Hlaing Myat; Hlaing, Hnin Wai Wai; Aung, Ni Htwe; Wai, Khaing Hsu; Thant, Hnin Aye; Min, Nandar Win

doi:10.5935/1981-4755.20190031

Cited by 1 publication

(2 citation statements)

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“…Two exceptions are found. Luqman et al [63] use Rule-based Machine Translation (RBMT) in 2020 to translate from Arabic sign language into Arabic, and Moe et al [60] compare NMT and SMT approaches for Myanmar sign language translation in 2018.…”

Section: Sign Language Mtmentioning

confidence: 99%

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Machine translation from signed to spoken languages: state of the art and challenges

Coster

Shterionov

Herreweghe

et al. 2023

Univ Access Inf Soc

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Automatic translation from signed to spoken languages is an interdisciplinary research domain on the intersection of computer vision, machine translation (MT), and linguistics. While the domain is growing in terms of popularity—the majority of scientific papers on sign language (SL) translation have been published in the past five years—research in this domain is performed mostly by computer scientists in isolation. This article presents an extensive and cross-domain overview of the work on SL translation. We first give a high level introduction to SL linguistics and MT to illustrate the requirements of automatic SL translation. Then, we present a systematic literature review of the state of the art in the domain. Finally, we outline important challenges for future research. We find that significant advances have been made on the shoulders of spoken language MT research. However, current approaches often lack linguistic motivation or are not adapted to the different characteristics of SLs. We explore challenges related to the representation of SL data, the collection of datasets and the evaluation of SL translation models. We advocate for interdisciplinary research and for grounding future research in linguistic analysis of SLs. Furthermore, the inclusion of deaf and hearing end users of SL translation applications in use case identification, data collection, and evaluation, is of utmost importance in the creation of useful SL translation models.

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Section: Sign Language Mtmentioning

confidence: 99%

“…Moe et al [60] compare RNNs and transformers for Gloss2Text with different tokenization schemes, and in every one of the experiments (on their own dataset), the transformer outperforms the RNN.…”

Section: Sign Language Translation Modelsmentioning

confidence: 99%