scholarly journals Morphological Theory and Sign Languages

2018 ◽  
pp. 593-614
Author(s):  
Donna Jo Napoli
Keyword(s):  
Physical Properties ◽  
Sign Language ◽  
Narrow Range ◽  
Visual Representation ◽  
Linguistic Analysis ◽  
Sign Languages ◽  
Language Typology ◽  
Morphological Theory ◽  
Per Se ◽  
Biomechanical Factors

Sign language morphology adds new considerations to well-studied areas, including category identification, inflection vs. derivation, the notions of ideophones, subject, and root, and properties used in lexical classifications. It makes necessary the new notion of reactive effort in understanding how biomechanical factors help shape the lexicon. The prevalence of simultaneity (verticality) over linearity (horizontal temporality) shows that linguistic analysis must include the study of physical properties (visual vs. auditory) if we are to understand language typology. Phonological parameters can have meaning associated with them, either arbitrarily or because they are iconic. This allows for lexical networks that require the mechanism of ion morphs. Certain phenomena are open to analysis as part of a system of visual representation needed in communication; that is, they are gestural and not part of language per se. So the grammar of sign languages covers a more narrow range of phenomena than that of spoken languages.

Weak hand holds in two sign languages and two genres

2015 ◽  
Vol 18 (2) ◽  
pp. 205-237 ◽  
Author(s):  
Anna Sáfár ◽  
Vadim Kimmelman
Keyword(s):  
Quantitative Analysis ◽  
The Netherlands ◽  
Sign Language ◽  
Linguistic Analysis ◽  
Genre Analysis ◽  
Sign Languages ◽  
Modality Effects ◽  
Corpus Data

In this paper, we provide a quantitative analysis of weak hand holds based on corpus data. We include both a cross-linguistic analysis of these holds in narrative data from Russian Sign Language (RSL) and Sign Language of the Netherlands (NGT), and a language-internal, cross-genre analysis comparing NGT narrative and conversational data. We classified the functions of all holds found in two corpora of RSL and NGT, and analyzed their formal characteristics. We found that holds in RSL and NGT have similar functions. However, holds are significantly more frequent in RSL than in NGT. In addition, we found that the distribution of holds across different functions varies between different genres in NGT. The similarities between RSL and NGT in the domain of holds may be attributed to modality effects. The differences in frequency of holds ask for a language-specific explanation, and we discuss several possible scenarios.

The point of agreement

2013 ◽  
Vol 16 (2) ◽  
pp. 221-258 ◽  
Author(s):  
Ronnie B. Wilbur
Keyword(s):  
Sign Language ◽  
Infinite Number ◽  
Real World ◽  
Linguistic Analysis ◽  
Clear Understanding ◽  
Sign Languages ◽  
Present Evidence ◽  
Language Structure ◽  
Person Marking

This paper reconsiders arguments suggesting that sign language analyses must proceed differently to take into account their gestural, iconic origins. Lillo-Martin & Meier (2011) argue that agreement is ‘person marking’, shown by directionality. Liddell (2003, 2011) argues that directional verbs move between locations associated with referents; given an infinite number of points, the forms of these verbs are unlistable, and therefore just gestural indicating; he claims that this makes sign languages different from spoken languages, a position that I will argue against. In their response, Lillo-Martin & Meier then agree that real-world referent locations are not part of grammar, so language must interface closely with the gestural system. In contrast, Quer (2011) argues that Liddell’s reasoning is flawed. I will present evidence to agree with Quer and argue that the linguistic discussion was prematurely derailed by noting the recent alternate analysis offered by Gökgöz (2013). There may well be a role for visual iconicity in relation to sign language structure, as demonstrated by Schlenker (2013a,b), but unless we pursue linguistic analysis further, we will never get a clear understanding of what that role is.

scholarly journals Interview with Diane Brentari

2020 ◽  
Vol 16 (3) ◽  
pp. 15-27
Author(s):  
Marília Uchôa Cavalcanti Lott de Moraes ◽  
Loise Soares de Azevedo ◽  
Anderson Almeida da Silva
Keyword(s):  
Sign Language ◽  
Historical Time ◽  
University Of Chicago ◽  
Sign Languages ◽  
Language Typology ◽  
Cambridge University ◽  
The University

Diane Brentari is co-director of the Center for Gesture, Sign and Language at the University of Chicago. She has published several books, such as Sign Language Phonology (Cambridge University Press, 2019) and Shaping Phonology (2018). Her research focuses on the role of modality in language, sign language phonology, how sign languages develop through historical time, and sign language typology.---------------------------------------------------------------------------------ENTREVISTA COM DIANE BRENTARIDiane Brentari é professora de linguística Mary K. Werkman e codiretora do Center for Gesture, Sign and Language da Universidade de Chicago. Publicou vários livros, como Sign Language Phonology (Cambridge University Press, 2019) e Shaping Phonology (2018). Sua pesquisa enfoca o papel da modalidade na linguagem, a fonologia da língua de sinais, como as línguas de sinais se desenvolvem ao longo do tempo histórico e a tipologia da língua de sinais.---Original em inglês.

Crosslinguistic similarity and variation in the simultaneous morphology of sign languages

2020 ◽  
Vol 37 (4) ◽  
pp. 571-608
Author(s):  
Diane Brentari ◽  
Laura Horton ◽  
Susan Goldin-Meadow
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Optimality Theory ◽  
Sign Languages ◽  
British Sign Language ◽  
Verb Phrases ◽  
Classifier Predicates ◽  
Crosslinguistic Variation ◽  
Hong Kong Sign Language ◽  
British Sign

Abstract Two differences between signed and spoken languages that have been widely discussed in the literature are: the degree to which morphology is expressed simultaneously (rather than sequentially), and the degree to which iconicity is used, particularly in predicates of motion and location, often referred to as classifier predicates. In this paper we analyze a set of properties marking agency and number in four sign languages for their crosslinguistic similarities and differences regarding simultaneity and iconicity. Data from American Sign Language (ASL), Italian Sign Language (LIS), British Sign Language (BSL), and Hong Kong Sign Language (HKSL) are analyzed. We find that iconic, cognitive, phonological, and morphological factors contribute to the distribution of these properties. We conduct two analyses—one of verbs and one of verb phrases. The analysis of classifier verbs shows that, as expected, all four languages exhibit many common formal and iconic properties in the expression of agency and number. The analysis of classifier verb phrases (VPs)—particularly, multiple-verb predicates—reveals (a) that it is grammatical in all four languages to express agency and number within a single verb, but also (b) that there is crosslinguistic variation in expressing agency and number across the four languages. We argue that this variation is motivated by how each language prioritizes, or ranks, several constraints. The rankings can be captured in Optimality Theory. Some constraints in this account, such as a constraint to be redundant, are found in all information systems and might be considered non-linguistic; however, the variation in constraint ranking in verb phrases reveals the grammatical and arbitrary nature of linguistic systems.

scholarly journals Interpretation of Swedish Sign Language Using Convolutional Neural Networks and Transfer Learning

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Gustaf Halvardsson ◽  
Johanna Peterson ◽  
César Soto-Valero ◽  
Benoit Baudry
Keyword(s):  
Neural Networks ◽  
Sign Language ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Web Application ◽  
Training Dataset ◽  
Motion Processing ◽  
Image Perception ◽  
Sign Languages ◽  
High Level

AbstractThe automatic interpretation of sign languages is a challenging task, as it requires the usage of high-level vision and high-level motion processing systems for providing accurate image perception. In this paper, we use Convolutional Neural Networks (CNNs) and transfer learning to make computers able to interpret signs of the Swedish Sign Language (SSL) hand alphabet. Our model consists of the implementation of a pre-trained InceptionV3 network, and the usage of the mini-batch gradient descent optimization algorithm. We rely on transfer learning during the pre-training of the model and its data. The final accuracy of the model, based on 8 study subjects and 9400 images, is 85%. Our results indicate that the usage of CNNs is a promising approach to interpret sign languages, and transfer learning can be used to achieve high testing accuracy despite using a small training dataset. Furthermore, we describe the implementation details of our model to interpret signs as a user-friendly web application.

scholarly journals Computer-assisted sign language translation: a study of translators’ practice to specify CAT software

2021 ◽  
Author(s):  
Marion Kaczmarek ◽  
Michael Filhol
Keyword(s):  
Sign Language ◽  
Language Translation ◽  
Computer Assisted ◽  
Translation Process ◽  
Sign Languages ◽  
Subjective Data ◽  
Do So

AbstractProfessional Sign Language translators, unlike their text-to-text counterparts, are not equipped with computer-assisted translation (CAT) software. Those softwares are meant to ease the translators’ tasks. No prior study as been conducted on this topic, and we aim at specifying such a software. To do so, we based our study on the professional Sign Language translators’ practices and needs. The aim of this paper is to identify the necessary steps in the text-to-sign translation process. By filming and interviewing professionals for both objective and subjective data, we build a list of tasks and see if they are systematic and performed in a definite order. Finally, we reflect on how CAT tools could assist those tasks, how to adapt the existing tools to Sign Language and what is necessary to add in order to fit the needs of Sign Language translation. In the long term, we plan to develop a first prototype of CAT software for sign languages.

The FATE Landscape of Sign Language AI Datasets

2021 ◽  
Vol 14 (2) ◽  
pp. 1-45
Author(s):  
Danielle Bragg ◽  
Naomi Caselli ◽  
Julie A. Hochgesang ◽  
Matt Huenerfauth ◽  
Leah Katz-Hernandez ◽  
...  
Keyword(s):  
Sign Language ◽  
New Technology ◽  
Individual Data ◽  
Community Members ◽  
Sign Languages ◽  
Language Data ◽  
History Of ◽  
Language Technologies ◽  
And Storage ◽  
Access Mechanisms

Sign language datasets are essential to developing many sign language technologies. In particular, datasets are required for training artificial intelligence (AI) and machine learning (ML) systems. Though the idea of using AI/ML for sign languages is not new, technology has now advanced to a point where developing such sign language technologies is becoming increasingly tractable. This critical juncture provides an opportunity to be thoughtful about an array of Fairness, Accountability, Transparency, and Ethics (FATE) considerations. Sign language datasets typically contain recordings of people signing, which is highly personal. The rights and responsibilities of the parties involved in data collection and storage are also complex and involve individual data contributors, data collectors or owners, and data users who may interact through a variety of exchange and access mechanisms. Deaf community members (and signers, more generally) are also central stakeholders in any end applications of sign language data. The centrality of sign language to deaf culture identity, coupled with a history of oppression, makes usage by technologists particularly sensitive. This piece presents many of these issues that characterize working with sign language AI datasets, based on the authors’ experiences living, working, and studying in this space.

People Are Less Susceptible to Illusion When They Use Their Hands to Communicate Rather Than Estimate

2021 ◽  
pp. 095679762199155
Author(s):  
Amanda R. Brown ◽  
Wim Pouw ◽  
Diane Brentari ◽  
Susan Goldin-Meadow
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
English Speakers ◽  
American Sign ◽  
Estimation Task ◽  
Sign Languages ◽  
Lyer Illusion ◽  
Description Task ◽  
The Way

When we use our hands to estimate the length of a stick in the Müller-Lyer illusion, we are highly susceptible to the illusion. But when we prepare to act on sticks under the same conditions, we are significantly less susceptible. Here, we asked whether people are susceptible to illusion when they use their hands not to act on objects but to describe them in spontaneous co-speech gestures or conventional sign languages of the deaf. Thirty-two English speakers and 13 American Sign Language signers used their hands to act on, estimate the length of, and describe sticks eliciting the Müller-Lyer illusion. For both gesture and sign, the magnitude of illusion in the description task was smaller than the magnitude of illusion in the estimation task and not different from the magnitude of illusion in the action task. The mechanisms responsible for producing gesture in speech and sign thus appear to operate not on percepts involved in estimation but on percepts derived from the way we act on objects.

scholarly journals Transitivity prominence within and across modalities

2019 ◽  
Vol 5 (1) ◽  
pp. 666-689
Author(s):  
Carl Börstell ◽  
Tommi Jantunen ◽  
Vadim Kimmelman ◽  
Vanja de Lint ◽  
Johanna Mesch ◽  
...  
Keyword(s):  
Sign Language ◽  
Sign Languages ◽  
Corpus Data ◽  
Direct Objects ◽  
Positive Correlations ◽  
Verb Meaning

AbstractWe investigate transitivity prominence of verbs across signed and spoken languages, based on data from both valency dictionaries and corpora. Our methodology relies on the assumption that dictionary data and corpus-based measures of transitivity are comparable, and we find evidence in support of this through the direct comparison of these two types of data across several spoken languages. For the signed modality, we measure the transitivity prominence of verbs in five sign languages based on corpus data and compare the results to the transitivity prominence hierarchy for spoken languages reported in Haspelmath (2015). For each sign language, we create a hierarchy for 12 verb meanings based on the proportion of overt direct objects per verb meaning. We use these hierarchies to calculate correlations between languages – both signed and spoken – and find positive correlations between transitivity hierarchies. Additional findings of this study include the observation that locative arguments seem to behave differently than direct objects judging by our measures of transitivity, and that relatedness among sign languages does not straightforwardly imply similarity in transitivity hierarchies. We conclude that our findings provide support for a modality-independent, semantic basis of transitivity.

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