Challenge: Several online dictionaries documenting the lexicon of a variety of sign languages are available . methodological issues must be addressed regarding how these resources are used for research purposes.
Approach: They propose a web-based tool for annotating the articulatory features of signs . they compare handshapes for four Asian SLs and handshape for the entire sample .
Outcome: The proposed tool compares handshapes and handsights of Asian SLs with European, American, and Brazilian SL samples.

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Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards (2026.acl-long)

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Challenge: Existing benchmarks fail to reflect real-world communication needs and are limited in their coverage.
Approach: They present a comprehensive index of sign-language datasets, covering 120 resources across 35 sign languages.
Outcome: The proposed index covers 120 resources across 35 sign languages.
IPSL: A Database of Iconicity Patterns in Sign Languages. Creation and Use (L18-1)

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Challenge: a database of signs annotated according to iconicity parameters was created . the database contains 1542 signs in 19 sign languages .
Approach: a team of researchers has created a large-scale database of iconic signs . the database contains 1542 signs in 19 sign languages .
Outcome: the database contains 1542 sign annotated in 19 sign languages . the database can be used to further study iconicity in sign languages.
How to Align Multiple Signed Language Corpora for Better Sign-to-Sign Translations? (2025.naacl-long)

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Challenge: despite the growing need for advanced signing technologies, signed language resources remain scarce.
Approach: They propose a linguistically informed alignment algorithm that matches instances between signed languages . they compare similarities and differences across three signed languages to develop a model .
Outcome: The proposed algorithm performs well on automatic metrics for sign-to-sign translation and generation.
WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language (2022.acl-short)

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Challenge: Signed Language Processing (SLP) is a major form of NLP, but has been overlooked by the NLP community.
Approach: They leverage existing resources to construct a large-scale dataset of American Sign Language signs annotated with six different phonological properties.
Outcome: The proposed model outperforms existing approaches on signs unobserved during training.
Including Signed Languages in Natural Language Processing (2021.acl-long)

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Challenge: Existing research in Sign Language Processing (SLP) rarely explores signed languages . authors urge adoption of an efficient tokenization method and the collection of real-world signed language data .
Approach: They propose to include signed languages as a research area with high social and scientific impact . they review the limitations of current SLP models and identify the open challenges .
Outcome: The proposed model should include signed languages as a research area with high social and scientific impact.
Automatic Gloss Dictionary for Sign Language Learners (2022.acl-demo)

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Challenge: 430 million people worldwide have developed hearing loss and 700 million more are learning a sign language as a second language . sign language learners have limited means of seeking assistance and are restricted to class offerings or relying on a webcam to look up the sign.
Approach: They propose an online tool supporting 2, 000 signs to assist language learners in determining the meaning of given signs.
Outcome: The proposed system can lower the barrier in sign language learning by addressing the common problem of sign finding and make it accessible to the wider community.
Open-Domain Sign Language Translation Learned from Online Video (2022.emnlp-main)

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Challenge: Existing work on sign language translation has focused mainly on data collected in controlled environments or domains, which limits its applicability to real-world settings.
Approach: They propose to use sign search as a pretext task and fusion of mouthing and handshape features to improve sign language translation in real-world settings.
Outcome: The proposed techniques produce consistent and large improvements over baseline models based on prior work.
Challenges with Sign Language Datasets for Sign Language Recognition and Translation (2022.lrec-1)

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Challenge: Sign Languages are the primary means of communication for at least half a million people in Europe . however, the development of SL recognition and translation tools is slowed down by resource scarcity and data formats are not suitable for machine learning.
Approach: They propose a framework to unify available resources and facilitate SL research for different languages.
Outcome: The proposed framework is based on a set of ELAN files and returns textual and visual data ready to train SL recognition and translation models.
Towards a new Ontology for Sign Languages (2022.lrec-1)

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Challenge: Linked Data (LD) compliant datasets for sign languages are not available in the LLOD cloud.
Approach: They propose to create an ontology for representing constitutive elements of Sign Languages (SL) they propose to publish such data in the Linguistic Linked Open Data cloud.
Outcome: The proposed ontology can be used to represent sign languages in the Linguistic Linked Open Data cloud.
Signbank: Software to Support Web Based Dictionaries of Sign Language (L18-1)

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Challenge: Auslan Signbank is an on-line dictionary for Australian Sign Language (Auslan) it was originally built to support the Auslan signbank web dictionary, but was re-implemented using Microsoft SQL Server.
Approach: This paper describes the overall architecture of the Auslan Signbank system and its representation of lexical entries and associated entities.
Outcome: The current version of Auslan Signbank is an open-source re-implementation of the original website, with features added to allow updates to the database by researchers.

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