An Improved Sign Language Translation Model with Explainable Adaptations for Processing Long Sign Sentences
المؤلفون المشاركون
Zhao, Zheng
Shi, Xiaodong
Zheng, Jiangbin
Wu, Chong
Chen, Yidong
Tong, Yiqi
Chen, Min
Chen, Jing
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-10-24
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Sign language translation (SLT) is an important application to bridge the communication gap between deaf and hearing people.
In recent years, the research on the SLT based on neural translation frameworks has attracted wide attention.
Despite the progress, current SLT research is still in the initial stage.
In fact, current systems perform poorly in processing long sign sentences, which often involve long-distance dependencies and require large resource consumption.
To tackle this problem, we propose two explainable adaptations to the traditional neural SLT models using optimized tokenization-related modules.
First, we introduce a frame stream density compression (FSDC) algorithm for detecting and reducing the redundant similar frames, which effectively shortens the long sign sentences without losing information.
Then, we replace the traditional encoder in a neural machine translation (NMT) module with an improved architecture, which incorporates a temporal convolution (T-Conv) unit and a dynamic hierarchical bidirectional GRU (DH-BiGRU) unit sequentially.
The improved component takes the temporal tokenization information into consideration to extract deeper information with reasonable resource consumption.
Our experiments on the RWTH-PHOENIX-Weather 2014T dataset show that the proposed model outperforms the state-of-the-art baseline up to about 1.5+ BLEU-4 score gains.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zheng, Jiangbin& Zhao, Zheng& Chen, Min& Chen, Jing& Wu, Chong& Chen, Yidong…[et al.]. 2020. An Improved Sign Language Translation Model with Explainable Adaptations for Processing Long Sign Sentences. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1138851
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zheng, Jiangbin…[et al.]. An Improved Sign Language Translation Model with Explainable Adaptations for Processing Long Sign Sentences. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1138851
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zheng, Jiangbin& Zhao, Zheng& Chen, Min& Chen, Jing& Wu, Chong& Chen, Yidong…[et al.]. An Improved Sign Language Translation Model with Explainable Adaptations for Processing Long Sign Sentences. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1138851
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1138851
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر