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Reimagining Sign Language Technologies: Analyzing Translation Work of Chinese Deaf Online Content Creators

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Tang, Xinru, Piper, Anne Marie·2026·Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3772318.3790624
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Abstract

While sign language translation systems promise to enhance deaf people’s access to information and communication, they have been met with strong skepticism from deaf communities due to risks of misrepresenting and oversimplifying the richness of signed communication in technologies. This article provides empirical evidence of the complexity of translation work involved in deaf communication through interviews with 13 deaf Chinese content creators who actively produce and share sign language content on video sharing platforms with both deaf and hearing audiences. By studying this unique group of content creators, our findings highlight the nuances of sign language translation, showing how deaf creators create content with multilingualism and multiculturalism in mind, support meaning making across languages and cultures, and navigate politics involved in their translation work. Grounded in these deaf-led translation practices, we draw on the sociolinguistic concept of (trans)languaging to re-conceptualize and reimagine the design of sign language translation systems.

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  • Hearing nonsigners may overlook critical linguistic elements in sign language that deaf people are familiar with, such as facial expressions.

    “For instance, hearing nonsigners may overlook critical linguistic elements in sign language that deaf people are familiar with, such as facial expressions [28, 97].”

    From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [97]

    Section 2 (p. 2) identifies facial expressions as visual-spatial elements that convey meaning in sign language, and the Introduction (p. 1) says sign-language gloves have been criticized for neglecting them. Section 5.2 (p. 9) also describes pressure to simplify signing for hearing audiences. The paper does not specifically say that hearing nonsigners themselves overlook facial expressions that deaf people recognize. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Many accessibility tasks require deeply personal embodied knowledge that disabled people possess, such as sign language translation and disabled people's non-normative communication practices.

    “Similarly, as explained in Section 8.1, many accessibility tasks require deeply personal embodied knowledge that disabled people possess, such as sign language translation [12, 95, 97], and disabled people's non-normative communication practices [3, 34, 41].”

    From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [97]

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Authors
Tang, X., & Piper, A. M.
Affiliations
University of California, Irvine (US)
ORCID
Venue
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2026-04-13
Pages
1-20
DOI
10.1145/3772318.3790624
OpenAlex
W7128714242
Cited by
1
References
0
Citations by year
2026: 1
FWCI
281.03
Type
conference paper
Language
en
Open access
Licence
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by-nc-nd)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Social Sciences › Arts and Humanities › Language and Linguistics › Translation Studies and Practices
  • Social Sciences › Psychology › Developmental and Educational Psychology › Hearing Impairment and Communication
  • Social Sciences › Arts and Humanities › Language and Linguistics › Subtitles and Audiovisual Media
Keywords
sign language translation, online video sharing platforms, translanguaging, multilingualism, multiculturalism, meaning-making, deaf community, translation and politics

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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