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

Khan, Nabeel Sabir; Abid, Adnan; Abid, Kamran

doi:10.1007/s12559-020-09731-7

Cited by 52 publications

(30 citation statements)

References 28 publications

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“…natural to sign language translation, and also for gesture recognition and converting it into natural language sentences. Particularly, for PSL, a natural to sign language translation model has been proposed by Khan et al in [41]. The manually translated output helps us in evaluating the translation system by comparing the translation output with the manually translated sentence available in the corpus while involving measures like BiLingual Evaluation Understudy Score (BLEU Score) and Word Error Rate (WER).…”

Section: Figure 13: Categories Of English Language Used To Build Sentence-level Translation Corpusmentioning

confidence: 99%

“…On the other hand, the same translation system helps generate semi-automatic sentence-level translation. Whereby, the involved subjects are shared the English language sentence as well as the translated sign language sentence with the help of the translation system proposed by Khan et al in [41]. In this case, the subjects have two options either to simply mark a sentence as correctly translated, or make the necessary change to the translation.…”

Section: Figure 13: Categories Of English Language Used To Build Sentence-level Translation Corpusmentioning

confidence: 99%

“…Figure 15 shows that the avatar-based video for a PSL sentence looks much better than the video or image-based rendered video, as its look and feel do not change while rendering the gestures. Again, to this end, this research seeks support from the translation system proposed by Khan et al in [41].…”

Section: Figure 13: Categories Of English Language Used To Build Sentence-level Translation Corpusmentioning

confidence: 99%

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A Crowdsourcing-Based Framework for the Development and Validation of Machine Readable Parallel Corpus for Sign Languages

et al. 2021

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Sign languages are used by the deaf and mute community of the world. These are gesture based languages where the subjects use hands and facial expressions to perform different gestures. There are hundreds of different sign languages in the world. Furthermore, like natural languages, there exist different dialects for many sign languages. In order to facilitate the deaf community several different repositories of video gestures are available for many sign languages of the world. These video based repositories do not support the development of an automated language translation systems. This research aims to investigate the idea of engaging the deaf community for the development and validation of a parallel corpus for a sign language and its dialects. As a principal contribution, this research presents a framework for building a parallel corpus for sign languages by harnessing the powers of crowdsourcing with editorial manager, thus it engages a diversified set of stakeholders for building and validating a repository in a quality controlled manner. It further presents processes to develop a word-level parallel corpus for different dialects of a sign language; and a process to develop sentence-level translation corpus comprising of source and translated sentences. The proposed framework has been successfully implemented and involved different stakeholders to build corpus. As a result, a word-level parallel corpus comprising of the gestures of almost 700 words of Pakistan Sign Language (PSL) has been developed. While, a sentencelevel translation corpus comprising of more than 8000 sentences for different tenses has also been developed for PSL. This sentence-level corpus can be used in developing and evaluating machine translation models for natural to sign language translation and vice-versa. While the machine-readable word level parallel corpus will help in generating avatar based videos for the translated sentences in different dialects of a sign language.

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Section: Figure 13: Categories Of English Language Used To Build Sentence-level Translation Corpusmentioning

confidence: 99%

Section: Figure 13: Categories Of English Language Used To Build Sentence-level Translation Corpusmentioning

confidence: 99%

Section: Figure 13: Categories Of English Language Used To Build Sentence-level Translation Corpusmentioning

confidence: 99%

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A Crowdsourcing-Based Framework for the Development and Validation of Machine Readable Parallel Corpus for Sign Languages

et al. 2021

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“…erefore, it is urgent to solve the problem of misplaced business English translation [2,3]. At present, a number of high-quality dictionary and translation types of software have emerged on the mobile platform, providing complete Chinese-English translation, word query, and other functions, but they are still limited to the traditional text translation field and have not touched the increasing demand urgent voice translation function [4,5]. Although some foreign translation software provides text-to-speech services, there are few translation tools to provide voiceto-text function, which still cannot fully meet the diversified translation needs of users [6].…”

Section: Introductionmentioning

confidence: 99%

Research on Business English Translation Framework Based on Speech Recognition and Wireless Communication

Wu²

2021

Mobile Information Systems

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In order to improve the accuracy of English translation, reduce the error rate of translation results, and increase the correction rate of translation, this paper proposes a business English translation architecture design based on speech recognition and wireless communication. The architecture is partitioned according to the functions of the overall system design, and the voice acquisition module, voice processing module, and peripheral circuit module are designed according to functional requirements. Among them, speech recognition helps users to perform language translation to reduce the possibility of errors in the translation process. At the same time, it uses wireless communication technology to construct a business English translation corpus to meet the personal needs of users. The paper also uses an improved translation model for translation error correction and intelligent proofreading, which improves the reliability of translation results. Experimental results show that the system has a high error correction rate and error correction rate, and the translation results have a certain degree of reliability, which fully verifies the effectiveness and application value of the system.

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“…Related Research. One of the classic problems in the field of natural language processing [20][21][22] is machine translation [23]. e development of machine translation is a microcosm of the development of natural language processing.…”

Section: Introductionmentioning

confidence: 99%

English Grammar Error Detection Using Recurrent Neural Networks

Zhen-hui

2021

Scientific Programming

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Automatic marking of English compositions is a rapidly developing field in recent years. It has gradually replaced teachers’ manual reading and become an important tool to relieve the teaching burden. The existing literature shows that the error of verb consistency and the error of verb tense are the two types of grammatical errors with the highest error rate in English composition. Hence, the detection results of verb errors can reflect the practicability and effectiveness of an automatic reading system. This paper proposes an English verb’s grammar error detection algorithm based on the cyclic neural network. Since LSTM can effectively retain the valid information in the context during training, this paper decided to use LSTM to model the labeled training corpus. At the same time, how to convert the text information in English compositions into numerical values for subsequent calculation is also an important step in automatic reading. Most mainstream tools use the word bag model, i.e., each word is encoded according to the order of each word in the dictionary. Although this encoding method is simple and easy to use, it not only causes the vector to lose the sequence information of the text but also is prone to dimensional disaster. Therefore, word embedding model is adopted in this paper to encode the text, and the text information is sequentially mapped to a low-dimensional vector space. In this way, the position information of the text is not lost, and the dimensional disaster is avoided. The proposed work collects some corpus samples and compares the proposed algorithm with Jouku and Bingguo. The verification results show the superiority of the proposed algorithm in verb error detection.

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

Cited by 52 publications

References 28 publications

A Crowdsourcing-Based Framework for the Development and Validation of Machine Readable Parallel Corpus for Sign Languages

A Crowdsourcing-Based Framework for the Development and Validation of Machine Readable Parallel Corpus for Sign Languages

Research on Business English Translation Framework Based on Speech Recognition and Wireless Communication

English Grammar Error Detection Using Recurrent Neural Networks

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