Collaborating with Language Community Members to Enrich Ethnographic Descriptions in a Language Archive

Mapping Intimacies ◽

10.12794/langarc1851172 ◽

2021 ◽

Author(s):

Mary Burke

Keyword(s):

South Asian ◽

Contextual Information ◽

Pilot Project ◽

Computational Resource ◽

Community Members ◽

Language Community ◽

Language Data ◽

Asian Languages

Language archives connect users such as language communities, linguists, and other researchers, to language data. As the language archiving community develops, concerns have been raised about the ethics, ownership, accessibility, and context of archival materials. While there are no simple solutions to these questions, many language archives are seeking ways to involve language community members in these conversations as they continue. This presentation describes a pilot project undertaken at the Computational Resource for South Asian Languages (CoRSAL) which explores a collaborative archiving approach to enable language community members to tell their own stories by adding contextual information to archival materials.

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Coda: Ten Questions on Globality

New Global Studies ◽

10.1515/ngs-2020-0031 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

The Editors ◽

Dipesh Chakrabarty

Keyword(s):

South Asian ◽

The State ◽

University Of Chicago ◽

Subaltern Studies ◽

Content Type ◽

Human Understanding ◽

The University ◽

Asian Languages

Abstract Dipesh Chakrabarty is Lawrence A. Kimpton Distinguished Service Professor in History and South Asian Languages and Civilizations at the University of Chicago. He is the author of several books, including The Crises of Civilization (2018) and Provincializing Europe (2000); and was one of the principal founders of the editorial collective of Subaltern Studies. In this discussion he ruminates upon the state of globality; its relationship to the planet Earth; the scope and possible duration of the Anthropocene; and some of globalization's consequences for humanity and human understanding. The interview was conducted by managing editor, Kenneth Weisbrode.

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The FATE Landscape of Sign Language AI Datasets

ACM Transactions on Accessible Computing ◽

10.1145/3436996 ◽

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.

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Structured (De)composable Representations Trained with Neural Networks

Computers ◽

10.3390/computers9040079 ◽

2020 ◽

Vol 9 (4) ◽

pp. 79

Author(s):

Graham Spinks ◽

Marie-Francine Moens

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Contextual Information ◽

Learning To Learn ◽

Class Label ◽

Impact Performance ◽

Novel Technique ◽

Language Data ◽

Concept Classes ◽

Generic Representation

This paper proposes a novel technique for representing templates and instances of concept classes. A template representation refers to the generic representation that captures the characteristics of an entire class. The proposed technique uses end-to-end deep learning to learn structured and composable representations from input images and discrete labels. The obtained representations are based on distance estimates between the distributions given by the class label and those given by contextual information, which are modeled as environments. We prove that the representations have a clear structure allowing decomposing the representation into factors that represent classes and environments. We evaluate our novel technique on classification and retrieval tasks involving different modalities (visual and language data). In various experiments, we show how the representations can be compressed and how different hyperparameters impact performance.

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