@inproceedings{khan:24043:sign-lang:lrec,
  author    = {Khan, Sarmad and Murtagh, Irene and McLoughlin, Simon D.},
  title     = {Investigating Motion History Images and Convolutional Neural Networks for Isolated {Irish} {Sign} {Language} Fingerspelling Recognition},
  pages     = {140--146},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources},
  maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)},
  publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}},
  address   = {Torino, Italy},
  day       = {25},
  month     = may,
  year      = {2024},
  isbn      = {978-2-493814-30-2},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24043.html},
  abstract  = {The limited global competency in sign language makes the objective of improving communication for the deaf and hard-of-hearing community through computational processing both vital and necessary. In an effort to address this problem, our research leverages the Irish Sign Language hand shape (ISL-HS) dataset and state-of-the-art deep learning architectures to recognize the Irish Sign Language alphabet. We streamline the feature extraction methodology and pave the way for the efficient use of Convolutional Neural Networks (CNNs) by using Motion History Images (MHIs) for monitoring the sign language motions. The effectiveness of numerous powerful CNN architectures in deciphering the intricate patterns of motion captured in MHIs is investigated in this research. The process includes generating MHIs from the ISL dataset and then using these images to train several CNN neural network models and evaluate their ability to recognize the Irish Sign Language alphabet. The results demonstrate the possibility of investigating MHIs with advanced CNNs to enhance sign language recognition, with a noteworthy accuracy percentage. By contributing to the development of language processing tools and technologies for Irish Sign Language, this research has the potential to address the lack of technological communicative accessibility and inclusion for the deaf and hard-of-hearing community in Ireland.}
}

@inproceedings{moiselle:22016:sign-lang:lrec,
  author    = {Moiselle, Rachel Ann and Leeson, Lorraine},
  title     = {Language Planning in Action: Depiction as a Driver of New Terminology in {Irish} {Sign} {Language}},
  pages     = {139--143},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC2022} 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources},
  maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {25},
  month     = jun,
  year      = {2022},
  isbn      = {979-10-95546-86-3},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/22016.html},
  abstract  = {In this paper, we examine the linguistic phenomenon known as `depiction', which relates to the ability to visually represent semantic components (Dudis, 2004). While some elements of this have been described for Irish Sign Language, with particular attention to the `productive lexicon' (Leeson {\&} Grehan, 2004; Leeson {\&} Saeed, 2012; Matthews, 1996; O'Baoill {\&} Matthews, 2000), here, we take the analysis further, drawing on what we have learned from cognitive linguistics over the past decade. Drawing on several recently developed domain-specific glossaries (e.g., STEM1, Covid-192, political domain, Sexual, Domestic and Gender Based Violence (SDGBV)-related vocabulary) we present ongoing analysis indicating that a deliberate focus on iconicity, in particular, elements of depiction, appears to be a primary driver. We also consider the potential implications of the insights we intend to gain from Deaf-led glossary glossary development work in the context of Machine Translation goals, for example, for work in progress on the Horizon 2020 funded SignON project.}
}

@inproceedings{holmes:70025:sltat:lrec,
  author    = {Holmes, Ruth and Rushe, Ellen and Fowley, Frank and Ventresque, Anthony},
  title     = {Improving Signer Independent Sign Language Recognition for Low Resource Languages},
  pages     = {45--52},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee},
  booktitle = {Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives},
  maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {24},
  month     = jun,
  year      = {2022},
  isbn      = {979-10-95546-82-5},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/2022.sltat-1.7.html},
  abstract  = {The reliance of deep learning algorithms on large scale datasets represents a significant challenge when learning from low resource sign language datasets. This challenge is compounded when we consider that, for a model to be effective in the real world, it must not only learn the variations of a given sign, but also learn to be invariant to the person signing. In this paper, we first illustrate the performance gap between signer-independent and signer-dependent models on Irish Sign Language manual hand shape data. We then evaluate the effect of transfer learning, with different levels of fine-tuning, on the generalisation of signer independent models, and show the effects of different input representations, namely variations in image data and pose estimation. We go on to investigate the sensitivity of current pose estimation models in order to establish their limitations and areas in need of improvement. The results show that accurate pose estimation outperforms raw RGB image data, even when relying on pre-trained image models. Following on from this, we investigate image texture as a potential contributing factor to the gap in performance between signer-dependent and signer-independent models using counterfactual testing images and discuss potential ramifications for low-resource sign languages. Keywords: Sign language recognition, Transfer learning, Irish Sign Language, Low-resource languages}
}

@inproceedings{sisto-etal-2022-challenges:lrec,
  author    = {De Sisto, Mirella and Vandeghinste, Vincent and Egea G{\'o}mez, Santiago and De Coster, Mathieu and Shterionov, Dimitar},
  title     = {Challenges with Sign Language Datasets for Sign Language Recognition and Translation},
  pages     = {2478--2487},
  editor    = {Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios},
  booktitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {20--25},
  month     = jun,
  year      = {2022},
  isbn      = {979-10-95546-72-6},
  language  = {english},
  url       = {https://aclanthology.org/2022.lrec-1.264},
  abstract  = {Sign Languages (SLs) are the primary means of communication for at least half a million people in Europe alone. However, the development of SL recognition and translation tools is slowed down by a series of obstacles concerning resource scarcity and standardization issues in the available data. The former challenge relates to the volume of data available for machine learning as well as the time required to collect and process new data. The latter obstacle is linked to the variety of the data, i.e., annotation formats are not unified and vary amongst different resources. The available data formats are often not suitable for machine learning, obstructing the provision of automatic tools based on neural models. In the present paper, we give an overview of these challenges by comparing various SL corpora and SL machine learning datasets. Furthermore, we propose a framework to address the lack of standardization at format level, unify the available resources and facilitate SL research for different languages. Our framework takes ELAN files as inputs and returns textual and visual data ready to train SL recognition and translation models. We present a proof of concept, training neural translation models on the data produced by the proposed framework.}
}

@inproceedings{hofmann:10010:sign-lang:lrec,
  author    = {Hofmann, Markus and Goslin, Kyle and Nolan, Brian and Leeson, Lorraine and Sheikh, Haaris},
  title     = {Development of a {Moodle} {VLE} Plug-in to Support Simultaneous Visualisation of a Collection of Multi-Media Sign Language Objects},
  pages     = {116--120},
  editor    = {Dreuw, Philippe and Efthimiou, Eleni and Hanke, Thomas and Johnston, Trevor and Mart{\'i}nez Ruiz, Gregorio and Schembri, Adam},
  booktitle = {Proceedings of the {LREC2010} 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies},
  maintitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Valletta, Malta},
  day       = {22--23},
  month     = may,
  year      = {2010},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/10010.html},
  abstract  = {Using Virtual Learning Environments (VLE) to support blended learning is very common in educational institutes. Delivering learning material in a flexible and semi-structured manner to the learner transforms such systems into powerful eLearning tools. However, the presentation and visualisation of individual or multiple learning objects is mostly dictated by the system and cannot be altered easily.
\par
This paper reports on a project between Trinity College Dublin (TCD) and the Institute of Technology Blanchardstown (ITB) that aims to improve the simultaneous visualisation of multiple multimedia objects for deaf learners of ISL. The project was implemented using the Open Source VLE Moodle. Moodle's nature of being Open Source and having the ability to code plug-ins qualified it to be the most suited vehicle to address the visualisation problem. Traditionally VLEs allow the viewing of one learning object at a time, which meant that deaf learners could either view a pre-recorded, signed in ISL, video lecture or concentrate on textual accompanying content but not both. The developed Moodle plug-in allows academics to group multiple videos into a 'lecture'. It further facilitates the addition of rich text content to each video. The learner can select and view one video from a possible sequence of many as well as view the text that belongs to the video. The paper further outlines detailed implementation and techniques applied.}
}

@inproceedings{morrissey:10032:sign-lang:lrec,
  author    = {Morrissey, Sara and Somers, Harold and Smith, Robert and Gilchrist, Shane and Dandapat, Sandipan},
  title     = {Building Sign Language Corpora for Use in Machine Translation},
  pages     = {172--177},
  editor    = {Dreuw, Philippe and Efthimiou, Eleni and Hanke, Thomas and Johnston, Trevor and Mart{\'i}nez Ruiz, Gregorio and Schembri, Adam},
  booktitle = {Proceedings of the {LREC2010} 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies},
  maintitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Valletta, Malta},
  day       = {22--23},
  month     = may,
  year      = {2010},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/10032.html},
  abstract  = {In recent years data-driven methods of machine translation (MT) have overtaken rule-based approaches as the predominant means of automatically translating between languages. A pre-requisite for such an approach is a parallel corpus of the source and target languages. Technological developments in sign language (SL) capturing, analysis and processing tools now mean that SL corpora are becoming increasingly available. With transcription and language analysis tools being mainly designed and used for linguistic purposes, we describe the process of creating a multimedia parallel corpus specifically for the purposes of English to Irish Sign Language (ISL) MT. As part of our larger project on localisation, our research is focussed on developing assistive technology for patients with limited English in the domain of healthcare. 
\par
Focussing on the first point of contact a patient has with a GP{\'i}s office, the medical secretary, we sought to develop a corpus from the dialogue between the two parties when scheduling an appointment. Throughout the development process we have created one parallel corpus in six different modalities from this initial dialogue, namely English speech, English text, ISL videos, Bangla text, HamNoSys transcription and SiGML code. In this paper we discuss the multi-stage process of the development of this parallel corpus as individual and interdependent entities, both for our own MT purposes and their usefulness in the wider MT and SL research domains.}
}

@inproceedings{thorvaldsdottir:10016:sign-lang:lrec,
  author    = {Thorvaldsdottir, Gudny Bjork},
  title     = {You Get Out What You Put In: The Beginnings of Phonetic and Phonological Coding in the Signs of {Ireland} Digital Corpus},
  pages     = {235--238},
  editor    = {Dreuw, Philippe and Efthimiou, Eleni and Hanke, Thomas and Johnston, Trevor and Mart{\'i}nez Ruiz, Gregorio and Schembri, Adam},
  booktitle = {Proceedings of the {LREC2010} 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies},
  maintitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Valletta, Malta},
  day       = {22--23},
  month     = may,
  year      = {2010},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/10016.html},
  abstract  = {This poster discusses a range of issues with respect to expanding the annotation of the Signs of Ireland (SOI) corpus to incorporate phonetic and phonological coding. This forms part of ongoing PHD research work that explores the phonology-morphology interface in Irish Sign Language (ISL).
\par
The SOI corpus consists of over 40 narratives that have already been highly annotated: it contains glossed lexical signs, classifier constructions and non-manual features. Classifier handshapes have also been annotated. It is my intention to identify the phonemes and the allophones of ISL using the corpus and it is thus neccessary to incorporate a detailed annotation at the phonetic level.
\par
In order to achieve this, a list of phonetic features for ISL must be identified. To date no research has been done in this area apart from basic work describing handshapes in ISL. Thus far, there is no agreement on the phonetic alphabet inventory for ISL: {\'O}'Baoill and Matthews (2000) identified 66 handshapes while Matthews (2005) identified 78. The issue of allophonic variation has not yet been tackled for this language. 
\par
For annotation purposes, challenges arise in terms of how handshapes are recorded: for example, of the 66 handshapes identified in {\'O}'Baoill and Matthews (2000), 28 are established as occurring as classifier handshapes also. These are annotated following ECHO project annotation norms (Nonhebel et al. 2004) where possible, with additional handshapes drawn from a list of 48 classifier handshapes described for BSL in Brennan (1992) using names like CL-B, CL-ISL-K etc. within the framework of the SOI corpus. 
\par
The other parameters that have traditionally been used to describe signs (i.e. location, movement and orientation) have not been researched in ISL at phonological or morphological level. All that currently exists is a vaguely phonetic level description of parameters respect to research on American Sign Languge (ASL) (See O'Baoill and Matthews 2000; Matthews 2005). 
\par
This poster outlines how, by drawing on Crasborn's (2001) and van der Kooij's (2002) work on Sign Language of the Netherlands (SLN), a list of phonetic features have been established for ISL and the changes to the original list of features that were required in order to accommodate ISL.
\par
I also outline the factors influencing decisions regarding the coding and naming of handshapes at phonetic level. These include the question of whether already established naming conventions be maintained. For example, moving away from established protocols will result in inconsistencies within the annotations in the corpus. However, for the purposes of phonetic research a more elaborate coding might be necessary. Another challenge involves establishing what types of tiers are needed to accommodate the proposed research as well as future research at the phonetic and phonological level.}
}

@inproceedings{herrmann:08015:sign-lang:lrec,
  author    = {Herrmann, Annika},
  title     = {Sign language corpora and the problems with {ELAN} and the {ECHO} annotation conventions},
  pages     = {68--73},
  editor    = {Crasborn, Onno and Efthimiou, Eleni and Hanke, Thomas and Thoutenhoofd, Ernst D. and Zwitserlood, Inge},
  booktitle = {Proceedings of the {LREC2008} 3rd Workshop on the Representation and Processing of Sign Languages: Construction and Exploitation of Sign Language Corpora},
  maintitle = {6th International Conference on Language Resources and Evaluation ({LREC} 2008)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marrakech, Morocco},
  day       = {1},
  month     = jun,
  year      = {2008},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/08015.html},
  abstract  = {Large corpus projects require logistic, technical and personal expertise and most importantly a conventionalized annotation system. In addition, relatively small projects with a definite set of data can also be an invaluable contribution to linguistic sign language research and therefore should use the same technical methods and annotation conventions for comparative reasons. The poster will present the process of building a corpus that is needed for a cross-linguistic study currently undertaken and focuses on the problems that arise with regard to annotation. The respective solutions shall be suggestions towards a unified convention.
\par
In this project, elicited data from three European sign languages and altogether 20 informants provide a set of approx. 900 sentences and short dialogues. Metadata information about participants and the recording situation will be edited in the IMDI metadata set. ELAN provides the most adequate annotation system for my purposes as the main interest of the study lies in the use of nonmanuals. The tool is widely used for sign language annotation and I try to guarantee for comparability by mainly adopting the ECHO annotation system with a few necessary adaptations.
\par
Problems listed below include repeatedly asked questions that are still not defined clearly yet:
\par
a) How are the on- and offsets of signs determined? Shall we annotate the separate signs or the signing stream integrating the transition period?
\par
b) How should pointing signs or constructions with many meaning components be transcribed?
\par
c) Despite more or less clear definitions of what each tier should be used for, the GLOSS-tier is sometimes intertwined with external information not fitting the tier. How can these problems be avoided?
\par
d) What kinds of disadvantages occur, if the eye gaze and eye blink annotations are not accurate?
\par
Possible Solutions:
\par
a) Even though the on- and offsets of signs can be defined more precisely than for words, the sign syllable not always has clear boundaries. Signing should be annotated as a streaming process that is interrupted when there is a hold or a significant pause. The transition from one sign to the other is often clearly visible through handshape change, which seems to be the more adequate marker for annotation. (The only problem left being sign duration, which cannot entirely be solved by the vague separate sign annotation either.)
\par
b) Proposal for a more detailed distinction of pointing signs without being theoretical (at least IX-1 for signer, IX-dual (excl., incl.) e.g.) and poly-meaning constructions (e.g. BE-LOCATED-CL:vehicle instead of (p-)vehicle-be-located; BLEAK instead of (p-)bleaking sheep when SHEEP is already introduced, decision between HOLD-CL:potato and HOLD-CL:round object).
\par
c) The GLOSS tier should only be used for manual signs or gestures, nonmanuals should not be included (*WALK- PURPOSEFUL). An additional tier is useful: other NMFs/look/other facial expressions
\par
d) Continuous eye gaze and eye aperture annotation is necessary to exactly determine eye gaze change with or without an eye blink and the duration and timing of blinks. This can especially be relevant for prosodic analysis.}
}

@inproceedings{leeson:08004:sign-lang:lrec,
  author    = {Leeson, Lorraine and Nolan, Brian},
  title     = {Digital Deployment of the Signs of {Ireland} Corpus in Elearning},
  pages     = {112--122},
  editor    = {Crasborn, Onno and Efthimiou, Eleni and Hanke, Thomas and Thoutenhoofd, Ernst D. and Zwitserlood, Inge},
  booktitle = {Proceedings of the {LREC2008} 3rd Workshop on the Representation and Processing of Sign Languages: Construction and Exploitation of Sign Language Corpora},
  maintitle = {6th International Conference on Language Resources and Evaluation ({LREC} 2008)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marrakech, Morocco},
  day       = {1},
  month     = jun,
  year      = {2008},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/08004.html},
  abstract  = {The Signs of Ireland corpus is part of the School of Linguistic, Speech and Communication Sciences' ``Languages of Ireland'' project. The first of its kind in Ireland, it comprises 40 male and female signers from across the Republic of Ireland, aged 18-65+, all of whom were educated in a school for the Deaf. The object was to create a snapshot of how ISL is used by `real' signers across geographic, gendered and generational boundaries, all of which have been indicated as sociolinguistically relevant for ISL (cf. the work of Le Master; also see Leeson and Grehan 2004, Leonard 2005, Leeson et al. 2006). With the aim of maximising the potential of cross-linguistic comparability, we mirrored aspects of data collection on other corpora collected to date. Thus, we include the Volterra et al. picture elicitation task (1984), ``The Frog Story'', and also asked informants to tell a self-selected story from their own life. To date, all of the self-selected stories have been annotated using ELAN.
\par
Two institutions (CDS, TCD and ITB) have partnered to create a unique elearning environment based on MOODLE as the learning management system. This delivers third level signed language programmes to a student constituency in a way that resolves problems of time, geography and access, maximizing multi-functional uses of the corpus across programmes. Students can take courseware synchronously and asynchronously. We have now built a considerable digital asset and plan to re-architect our framework to avail of current best practice in digital repositories and digital learning objects vis-{\`a}-vis Irish Sign Language.
\par
This paper outlines the establishment and annotation of the corpus, and the success of the corpus to date in supporting curricula and research. This paper focuses on moving the corpus forward as an asset to develop digital teaching objects. This paper outlines the challenges inherent in this process, and outlines our plans and our progress to date in meeting these objectives. Specific issues include:\begin{itemize}\item Decisions regarding annotation\item Establishing mark-up standards\item Use of the Signs of Ireland corpus in elearning/ blended learning contexts\item Leveraging a corpus within digital learning objects\item Architecture of a digital repository to support sign language learning\item Tagging of learning objects versus language objects\item Issues of assessment in an elearning context\end{itemize}
\par
References
\par
Lorraine Leeson, John Saeed, Cormac Leonard, Alison Macduff and Deirdre Byrne-Dunne 2006: Moving Heads and Moving Hands: Developing a Digital Corpus of Irish Sign Language: The `Signs of Ireland' Corpus Development Project. Paper presented at the IT{\&}T conference, Carlow, 2006.
\par
Leeson, L. and C. Grehan 2004: To the Lexicon and Beyond: The Effect of Gender on Variation in Irish Sign Language. In M. Van Herreweghe and M. Vermeerbergen (eds.): To The Lexicon and Beyond: The Sociolinguistics of European Sign Languages. Gallaudet University Press. 39-73.
\par
Le Master, B. 1990: The Maintenance and Loss of Female and Male Signs in the Dublin Deaf Community. PhD Dissertation. Los Angeles: University of California.
\par
Le Master,B. 2002: What Difference Does Difference Make? Negotiating Gender and Generation in Irish Sign Language. In S. Benor, M. Rose, D. Sharma and Q. Shang (eds.): Gendered Practices in Language. Centre for the Study of Languages and Information Publication. Stanford.
\par
Leonard, C. 2005: Signs of Diversity: Use and Recognition of Gendered Signs among Young Irish Deaf People. Deaf Worlds, Vol. 21 (2) 62-77.
\par
Volterra, V., S. Laudanna, E. Corazza and F. Natale 1984: Italian Sign Language: The Order of Elements in the Declarative Sentence. In F. Lonke (ed.) Recent Research on European Sign Languages. Svets and Zeitlinger: Lisse. 19- 48.}
}

@inproceedings{bungeroth-etal-2008-atis:lrec,
  author    = {Bungeroth, Jan and Stein, Daniel and Dreuw, Philippe and Ney, Hermann and Morrissey, Sara and Way, Andy and van Zijl, Lynette},
  title     = {The {ATIS} Sign Language Corpus},
  pages     = {2943--2946},
  editor    = {Calzolari, Nicoletta and Choukri, Khalid and Maegaard, Bente and Mariani, Joseph and Odijk, Jan and Piperidis, Stelios and Tapias, Daniel},
  booktitle = {6th International Conference on Language Resources and Evaluation ({LREC} 2008)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marrakech, Morocco},
  day       = {26},
  month     = may,
  year      = {2008},
  isbn      = {978-2-9517408-4-6},
  language  = {english},
  url       = {https://aclanthology.org/L08-1470},
  abstract  = {Systems that automatically process sign language rely on appropriate data. We therefore present the ATIS sign language corpus that is based on the domain of air travel information. It is available for five languages, English, German, Irish sign language, German sign language and South African sign language. The corpus can be used for different tasks like automatic statistical translation and automatic sign language recognition and it allows the specific modeling of spatial references in signing space.}
}