The Corpus Encoding Standard (CES) is a part of the EAGLES Guidelines developed by the Expert Advisory Group on Language Engineering Standards (EAGLES) that provides a set of encoding standards for corpus-based work in natural language processing applications. We have instantiated the CES as an XML application called XCES, based on the same data architecture comprised of a primary encoded text and "standoff" annotation in separate documents. Conversion to XML enables use of some of the more powerful mechanisms provided in the XML framework, including the XSLT Transformation Language, XML Schemas, and support for interrescue reference together with an extensive path syntax for pointers. In this paper, we describe the differences between the CES and XCES DTDs and demonstrate how XML mechanisms can be used to select from and manipulate annotated corpora encoded according to XCES specifications. We also provide a general overview of XML and the XML mechanisms that are most relevant to language engineering research and applications.

XCES : An XML-based Encoding Standard for Linguistic Corpora XML Conversion of the CES

The goal of automatic Sign Language Production (SLP) is to translate spoken language to a continuous stream of sign language video at a level comparable to a human translator. If this was achievable, then it would revolutionise Deaf hearing communications. Previous work on predominantly isolated SLP has shown the need for architectures that are better suited to the continuous domain of full sign sequences. 
In this paper, we propose Progressive Transformers, a novel architecture that can translate from discrete spoken language sentences to continuous 3D skeleton pose outputs representing sign language. We present two model configurations, an end-to-end network that produces sign direct from text and a stacked network that utilises a gloss intermediary. 
Our transformer network architecture introduces a counter that enables continuous sequence generation at training and inference. We also provide several data augmentation processes to overcome the problem of drift and improve the performance of SLP models. We propose a back translation evaluation mechanism for SLP, presenting benchmark quantitative results on the challenging RWTH-PHOENIX-Weather-2014T(PHOENIX14T) dataset and setting baselines for future research.

Progressive Transformers for End-to-End Sign Language Production

Sign languages are used by the deaf community around the globe to communicate with one another. These are gesture-based languages where a deaf person performs gestures using hands and facial expressions. Every gesture represents a word or a phrase in the natural language. There are more than 200 different sign languages in the world. In order to facilitate the learning of sign languages by the deaf community, researchers have compiled sign language repositories comprising of gestures. Similarly, algorithms have been proposed to translate the natural language into sign language, which is subsequently converted into gestures using avatar technology. On the other hand, several different approaches for gesture recognition have also been proposed in the literature, many of which use specialized hardware. Similarly, cell phone applications have been developed for learning and translation of sign languages. This article presents a systematic literature review of these multidisciplinary aspects of sign language translation. It provides a detailed analysis of carefully selected 147 high-quality research articles and books related to the subject matter. Specifically, it categorizes different approaches used for each component, discusses their theoretical foundations, and provides a comparative analysis of the proposed approaches. Lastly, open research challenges and future directions for each facet of the sign language translation problem have been discussed. To the best of our knowledge, this is the first comprehensive survey on sign language translation that discusses state-of-the-art research from multi-disciplinary perspectives.

Advances in machine translation for sign language: approaches, limitations, and challenges

The deaf community in the world uses a gesture-based language, generally known as sign language. Every country has a different sign language; for instance, USA has American Sign Language (ASL) and UK has British Sign Language (BSL). The deaf community in Pakistan uses Pakistan Sign Language (PSL), which like other natural languages, has a vocabulary, sentence structure, and word order. Majority of the hearing community is not aware of PSL due to which there exists a huge communication gap between the two groups. Similarly, deaf persons are unable to read text written in English and Urdu. Hence, the provision of an effective translation model can support the cognitive capability of the deaf community to interpret natural language materials available on the Internet and in other useful resources. This research involves exploiting natural language processing (NLP) techniques to support the deaf community by proposing a novel machine translation model that translates English sentences into equivalent Pakistan Sign Language (PSL). Though a large number of machine translation systems have been successfully implemented for natural to natural language translations, natural to sign language machine translation is a relatively new area of research. State-of-the-art works in natural to sign language translation are mostly domain specific and suffer from low accuracy scores. Major reasons are specialised language structures for sign languages, and lack of annotated corpora to facilitate development of more generalisable machine translation systems. To this end, a grammar-based machine translation model is proposed to translate sentences written in English language into equivalent PSL sentences. To the best of our knowledge, this is a first effort to translate any natural language to PSL using core NLP techniques. The proposed approach involves a structured process to investigate the linguistic structure of PSL and formulate the grammatical structure of PSL sentences. These rules are then formalised into a context-free grammar, which, in turn, can be efficiently implemented as a parsing module for translation and validation of target PSL sentences. The whole concept is implemented as a software system, comprising the NLP pipeline and an external service to render the avatar-based video of translated words, in order to compensate the cognitive hearing deficit of deaf people. The accuracy of the proposed translation model has been evaluated manually and automatically. Quantitative results reveal a very promising Bilingual Evaluation Understudy (BLEU) score of 0.78. Subjective evaluations demonstrate that the system can compensate for the cognitive hearing deficit of end users through the system output expressed as a readily interpretable avatar. Comparative analysis shows that our proposed system works well for simple sentences but struggles to translate compound and compound complex sentences correctly, which warrants future ongoing research.

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

An equal opportunity for all is the basic right of every human being. The deaf society of the world needs to have access to all the information just like hearing people do. For this to happen there should be a mode of direct communication between hearing and deaf people. The need at this time is to automate this communication so as the deaf society is not dependent upon human interpreters. This paper deals with the systematic survey of conventional and state-of-the-art sign language machine translation and sign language generation projects. We used a standard procedure of carrying out a systematic literature review on 148 studies published in 30 reputed journals and 40 premium conferences and workshops. Existing literature about sign language machine translation is broadly classified into three different categories. These categories are further sub-classified into different classifications depending upon the type of machine translation. Studies pertaining to the specified classifications have been presented with their advantages and limitations. Different methods for sign language generation are reported with their benefits and limitations. Manual and automatic evaluation methods used in every study is presented along with their respective performance metrics. We call for increased efforts in presentation of signs to make them an easy and comfortable mode of communication for deaf society. There is also a requirement to improve translation methods and include the contribution of advanced technologies such as deep learning and neural networks to make an optimal translation process between text and sign language.

Machine translation from text to sign language: a systematic review

A novel rule based machine translation scheme from Greek to Greek Sign Language: Production of different types of large corpora and Language Models evaluation

This paper proposes the implementation of a post-processing stage to a grammar-based machine translation (MT) system from written into sign language for the pair written Greek---Greek sign language (GSL), where translation output is visualized by an avatar. Post-processing is applied on the output of the MT system's transfer module by activating an editing environment for SL which utilizes a dictionary of signs coded for HamNoSys transcriptions and non-manual elements along with grammar knowledge. This post-processing environment may also be used as a standalone editing environment for expert users allowing for a considerable range of editing actions, as well as an SL editing tool for the general public who are not familiar with the underlying technology. In both cases, this environment provides the opportunity of on-the-fly structuring of new signed utterances going far beyond current limitations in composition with new signed language content.

From grammar-based MT to post-processed SL representations

In this article, a prototype Greek text to Greek Sign Language (GSL) conversion system is presented. The system is integrated into an educational platform that addresses the needs of teaching GSL grammar and was developed within the SYNENNOESE project (Efthimiou et al. 2004a. Developing an e-learning platform for the Greek sign language. In: K. Miesenberger, J. Klaus, and W. Zagler, eds. Computer helping people with special needs, Lecture Notes in Computer Science, Springer-Verlag, vol. 3118, 1107-1113). The detailed implementation of the language-processing component of a Greek text to GSL conversion system is provided, focusing upon the inherent problems of knowledge elicitation of sign language (SL) grammar and its implementation within a parser framework. It is based on an SL dictionary (Efthimiou et al. 2004a) database of coded GSL knowledge. The proposed system has been designed and implemented after considering most state-of-the-art SL machine translation or Conversion systems, such as Vsigns (Papadogiorgaki et al. 2004. VSigns-a virtual sign synthesis web tool. In: Proceedings of Sixth COST 276 Workshop on Information and Knowledge Management for Integrated Media Communication, May 2004, Thessaloniki, Greece), ZARDOZ (Veale et al. 1998. The challenges of cross-modal translation: English to sign language translation in the ZARDOZ system. Machine Translation, 13, 81-106) and SignSynth (Angus 2001. SignSynth: a sign language synthesis application using Web3D and Perl. In: Gesture and Sign Lanauage in Human-Computer Interaction. London, UK: International Gesture Workshop), and taking into account their advantages and disadvantages. The overall architecture is innovative since other existing systems either do not consider the GSL or they cannot be effectively applied on sentences but just on single words. The system is demonstrable on any conventional PC.

A prototype Greek text to Greek Sign Language conversion system

One of the objectives of Assistive Technologies is to help people with disabilities communicate with others and provide means of access to information. As an aid to Deaf people, we present in this work a novel prototype Rule-Based Machine Translation (RBMT) system for the creation of large quality written Greek text to Greek Sign Language (GSL) glossed corpora. In particular, the proposed RBMT system supports the professional translator of GSL to produce high quality parallel Greek text GSL glossed corpus, which is then used as training data by the Statistical Machine Translation (SMT) MOSES [1] application system. It should be noted that the whole process is robust and flexible, since it does not demand deep grammar knowledge of GSL. With this work we manage to overcome the two biggest obstacles in Natural Processing Language (NLP) of GSL. Firstly, the lack of written system and secondly the lack of grammar and finally we have been able to lay the foundations for an autonomous translation system of Greek text to GSL. Evaluation of the proposed scheme is carried out in the weather reports domain, where 20,284 tokens and 1,000 sentences have been produced. By using the BiLingual Evaluation Understudy (BLEU) metric score, our prototyped MT system achieves a relative average score of 60.53% and 85.1%/65.5%/53.8%/44.8% for for 1-gram/2gram/3-gram/4-gram evaluation.

Statistical Machine Translation for Greek to Greek Sign Language Using Parallel Corpora Produced via Rule-Based Machine Translation.

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A novel rule based machine translation scheme from Greek to Greek Sign Language: Production of different types of large corpora and Language Models evaluation

From grammar-based MT to post-processed SL representations

A prototype Greek text to Greek Sign Language conversion system

Statistical Machine Translation for Greek to Greek Sign Language Using Parallel Corpora Produced via Rule-Based Machine Translation.