A Novel Natural Language Processing (NLP)–Based Machine Translation Model for English to Pakistan Sign Language Translation

2020 ◽  
Vol 12 (4) ◽  
pp. 748-765
Author(s):  
Nabeel Sabir Khan ◽  
Adnan Abid ◽  
Kamran Abid
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sign Language ◽  
Machine Translation ◽  
Language Processing ◽  
Language Translation ◽  
Translation Model

PhraseAttn: Dynamic Slot Capsule Networks for phrase representation in Neural Machine Translation

2021 ◽  
pp. 1-8
Author(s):  
Binh Nguyen ◽  
Binh Le ◽  
Long H.B. Nguyen ◽  
Dien Dinh
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Vital Role ◽  
Attention Mechanism ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Word Representation

 Word representation plays a vital role in most Natural Language Processing systems, especially for Neural Machine Translation. It tends to capture semantic and similarity between individual words well, but struggle to represent the meaning of phrases or multi-word expressions. In this paper, we investigate a method to generate and use phrase information in a translation model. To generate phrase representations, a Primary Phrase Capsule network is first employed, then iteratively enhancing with a Slot Attention mechanism. Experiments on the IWSLT English to Vietnamese, French, and German datasets show that our proposed method consistently outperforms the baseline Transformer, and attains competitive results over the scaled Transformer with two times lower parameters.

On the Statistical Machine Translation Studies

2013 ◽  
Vol 347-350 ◽  
pp. 3262-3266
Author(s):  
Ai Ling Wang
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Large Scale ◽  
Statistical Machine Translation ◽  
Translation Model ◽  
Statistical Natural Language Processing ◽  
Translation Methods ◽  
Important Branch

Machine translation (MT) is one of the core application of natural language processing and an important branch of artificial intelligence research; statistical methods have already become the mainstream of machine translation. This paper explores the comparative analysis on the translation model of statistical natural language processing based on the large-scale corpus; discusses word-based, phrase-based and syntax-based machine translation methods respectively, summarizes the evaluation factors of machine translation and analyzes evaluation methods of machine translation.

Deep Natural Language Processing for Italian Sign Language Translation

2013 ◽  
pp. 193-204 ◽  
Author(s):  
Alessandro Mazzei ◽  
Leonardo Lesmo ◽  
Cristina Battaglino ◽  
Mara Vendrame ◽  
Monica Bucciarelli
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sign Language ◽  
Language Processing ◽  
Language Translation

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

scholarly journals Analysis of Japanese-Japanese Sign Language Dictionary Using Natural Language Processing

2008 ◽  
Vol 17 (0) ◽  
pp. 85-92
Author(s):  
Tomohiro Kuroda ◽  
Kazuya Okamoto ◽  
Tadamasa Takemura ◽  
Naoki Oboshi ◽  
Yoshihiro Kuroda ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sign Language ◽  
Language Processing ◽  
Japanese Sign Language
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