Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSign Language Translation in a Healthcare Setting
ترجمة لغة الإشارة في إعداد الرعاية الصحية
480
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
التواصل بين أخصائيي الرعاية الصحية والمرضى الصم يتحدىون، والوباء الحالي Covid-19 يجعل هذه المشكلة أكثر حادة.غالبا ما لا تدخل مفسر لغة الإشارة في كثير من الأحيان المستشفيات وأقنائك الوجهلمعالجة هذه المشكلة العاجلة، قمنا بتطوير نظام يسمح بمهنيي الرعاية الصحية بترجمة الجمل التي يتم استخدامها بشكل متكرر في تشخيص وعلاج CovID-19 في لغة هولندا (NGT).يتم عرض الترجمات عن طريق مقاطع الفيديو والرسوم المتحركة الرمزية.بنية النظام هي بحيث يمكن تمديدها إلى تطبيقات أخرى ولغات تسجيل أخرى بطريقة واضحة نسبيا.
Communication between healthcare professionals and deaf patients is challenging, and the current COVID-19 pandemic makes this issue even more acute. Sign language interpreters can often not enter hospitals and face masks make lipreading impossible. To address this urgent problem, we developed a system which allows healthcare professionals to translate sentences that are frequently used in the diagnosis and treatment of COVID-19 into Sign Language of the Netherlands (NGT). Translations are displayed by means of videos and avatar animations. The architecture of the system is such that it could be extended to other applications and other sign languages in a relatively straightforward way.
References used
https://aclanthology.org/
rate research
Read More
A cascaded Sign Language Translation system first maps sign videos to gloss annotations and then translates glosses into a spoken languages. This work focuses on the second-stage gloss translation component, which is challenging due to the scarcity o
f publicly available parallel data. We approach gloss translation as a low-resource machine translation task and investigate two popular methods for improving translation quality: hyperparameter search and backtranslation. We discuss the potentials and pitfalls of these methods based on experiments on the RWTH-PHOENIX-Weather 2014T dataset.
Sign language translation (SLT) is often decomposed into video-to-gloss recognition and gloss to-text translation, where a gloss is a sequence of transcribed spoken-language words in the order in which they are signed. We focus here on gloss-to-text
translation, which we treat as a low-resource neural machine translation (NMT) problem. However, unlike traditional low resource NMT, gloss-to-text translation differs because gloss-text pairs often have a higher lexical overlap and lower syntactic overlap than pairs of spoken languages. We exploit this lexical overlap and handle syntactic divergence by proposing two rule-based heuristics that generate pseudo-parallel gloss-text pairs from monolingual spoken language text. By pre-training on this synthetic data, we improve translation from American Sign Language (ASL) to English and German Sign Language (DGS) to German by up to 3.14 and 2.20 BLEU, respectively.
This paper presents an overview of AVASAG; an ongoing applied-research project developing a text-to-sign-language translation system for public services. We describe the scientific innovation points (geometry-based SL-description, 3D animation and video corpus, simplified annotation scheme, motion capture strategy) and the overall translation pipeline.
One of the major challenges in sign language translation from a sign language to a spoken language is the lack of parallel corpora. Recent works have achieved promising results on the RWTH-PHOENIX-Weather 2014T dataset, which consists of over eight t
housand parallel sentences between German sign language and German. However, from the perspective of neural machine translation, this is still a tiny dataset. To improve the performance of models trained on small datasets, transfer learning can be used. While this has been previously applied in sign language translation for feature extraction, to the best of our knowledge, pretrained language models have not yet been investigated. We use pretrained BERT-base and mBART-50 models to initialize our sign language video to spoken language text translation model. To mitigate overfitting, we apply the frozen pretrained transformer technique: we freeze the majority of parameters during training. Using a pretrained BERT model, we outperform a baseline trained from scratch by 1 to 2 BLEU-4. Our results show that pretrained language models can be used to improve sign language translation performance and that the self-attention patterns in BERT transfer in zero-shot to the encoder and decoder of sign language translation models.
There has been significant progress in dialogue systems research. However, dialogue systems research in the healthcare domain is still in its infancy. In this paper, we analyse recent studies and outline three building blocks of a task-oriented dialo
gue system in the healthcare domain: i) privacy-preserving data collection; ii) medical knowledge-grounded dialogue management; and iii) human-centric evaluations. To this end, we propose a framework for developing a dialogue system and show preliminary results of simulated dialogue data generation by utilising expert knowledge and crowd-sourcing.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
