Chapter 7. Exploring regional variation in the DGS Corpus

Hanke, Thomas; Konrad, Reiner; Langer, Gabriele

doi:10.1075/scl.108.07han

Cited by 9 publications

(18 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shi's model [82] only translates phrases correctly when they occur in the training set, suggesting overfitting. Angelova et al use the DGS corpus [280] (which contains discourse on general topics) as a dataset; they also obtain much lower translation scores than on RWTH-PHOENIX-Weather 2014T [91].…”

Section: Discussionmentioning

confidence: 99%

“…Yin et al [65] find that a transformer outperforms RNNs and that an RNN with Luong attention outperforms one with Bahdanau attention. Angelova et al [91] achieve higher scores with RNNs than with transformers (on the DGS corpus [280] as well). Finally, Camgöz et al [37] report a large increase in BLEU scores when using transformers, compared to their previous paper using RNNs [6].…”

Section: Sign Language Translation Modelsmentioning

confidence: 97%

See 1 more Smart Citation

Machine translation from signed to spoken languages: state of the art and challenges

Coster

Shterionov

Herreweghe

et al. 2023

Univ Access Inf Soc

View full text Add to dashboard Cite

Automatic translation from signed to spoken languages is an interdisciplinary research domain on the intersection of computer vision, machine translation (MT), and linguistics. While the domain is growing in terms of popularity—the majority of scientific papers on sign language (SL) translation have been published in the past five years—research in this domain is performed mostly by computer scientists in isolation. This article presents an extensive and cross-domain overview of the work on SL translation. We first give a high level introduction to SL linguistics and MT to illustrate the requirements of automatic SL translation. Then, we present a systematic literature review of the state of the art in the domain. Finally, we outline important challenges for future research. We find that significant advances have been made on the shoulders of spoken language MT research. However, current approaches often lack linguistic motivation or are not adapted to the different characteristics of SLs. We explore challenges related to the representation of SL data, the collection of datasets and the evaluation of SL translation models. We advocate for interdisciplinary research and for grounding future research in linguistic analysis of SLs. Furthermore, the inclusion of deaf and hearing end users of SL translation applications in use case identification, data collection, and evaluation, is of utmost importance in the creation of useful SL translation models.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Sign Language Translation Modelsmentioning

confidence: 97%

Machine translation from signed to spoken languages: state of the art and challenges

Coster

Shterionov

Herreweghe

et al. 2023

Univ Access Inf Soc

View full text Add to dashboard Cite

show abstract

“…Several experiments are then conducted in terms of classifiers: different linear baselines with a fixed context and recurrent models with different counts of input features. [9] introduces the DGS Corpus [17] for sign detection and achieves 91.53% accuracy on the test set.…”

Section: Related Workmentioning

confidence: 99%

“…Its total footage contains over 1150 hours of recordings, of which only a part is annotated. The Public DGS Corpus [17] is a subset of about 50 hours of the DGS Corpus intended for public release. [9] used it to propose a train, dev and test split for the task of sign language detection.…”

Section: Dgs Corpusmentioning

confidence: 99%

On the Importance of Signer Overlap for Sign Language Detection

Pal¹,

Huber²,

Chaabani³

et al. 2023

Preprint

View full text Add to dashboard Cite

Sign language detection, identifying if someone is signing or not, is becoming crucially important for its applications in remote conferencing software and for selecting useful sign data for training sign language recognition or translation tasks. We argue that the current benchmark data sets for sign language detection estimate overly positive results that do not generalize well due to signer overlap between train and test partitions. We quantify this with a detailed analysis of the effect of signer overlap on current sign detection benchmark data sets. Comparing accuracy with and without overlap on the DGS corpus and Signing in the Wild, we observed a relative decrease in accuracy of 4.17% and 6.27%, respectively. Furthermore, we propose new data set partitions that are free of overlap and allow for more realistic performance assessment. We hope this work will contribute to improving the accuracy and generalization of sign language detection systems.

show abstract

“…Even specific linguistic studies that address issues for the development of vocabulary tests for DSGS are rare. Examples of sign languages for which corpora of different sizes have been compiled include BSL (Fenlon et al, 2014), Auslan (Johnston & Schembri, 2006), and DGS (Hanke, 2016). New Zealand Sign Language (NZSL) is an example of a well-researched sign language that has a reference grammar upon which test developers can draw on (McKee, 2015) when developing tests.…”

Section: Issues In Developing Sign Language Assessmentsmentioning

confidence: 99%

Challenges in rating signed production: A mixed-methods study of a Swiss German Sign Language form-recall vocabulary test

et al. 2022

View full text Add to dashboard Cite

Sign languages present particular challenges to language assessors in relation to variation in signs, weakly defined citation forms, and a general lack of standard-setting work even in long-established measures of productive sign proficiency. The present article addresses and explores these issues via a mixed-methods study of a human-rated form-recall sign vocabulary test of 98 signs for beginning adult learners of Swiss German Sign Language (DSGS), using post-test qualitative rater interviews to inform interpretation of the results of quantitative analysis of the test ratings using many-facets Rasch measurement. Significant differences between two expert raters were observed on three signs. The follow-up interview revealed disagreement on the criterion of correctness, despite the raters’ involvement in the development of the base lexicon of signs. The findings highlight the challenges of using human ratings to assess the production not only of sign language vocabulary, but of minority languages generally, and underscore the need for greater effort expended on the standardization of sign language assessment.

show abstract

Chapter 7. Exploring regional variation in the DGS Corpus

Cited by 9 publications

References 12 publications

Machine translation from signed to spoken languages: state of the art and challenges

Machine translation from signed to spoken languages: state of the art and challenges

On the Importance of Signer Overlap for Sign Language Detection

Challenges in rating signed production: A mixed-methods study of a Swiss German Sign Language form-recall vocabulary test

Contact Info

Product

Resources

About