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Onur Keleş

@onurkeles.bsky.social

PhD Student of Linguistics, Research Assistant at Bogazici University | Interested in visual-gestural modality, quantitative linguistics, and natural language processing

Posts Replies Media

Onur Keleş @onurkeles.bsky.social · 15/10/2025

I’m very happy to share our new paper! “The Visual Iconicity Challenge: Evaluating Vision–Language Models on Sign Language Form–Meaning Mapping”, co-authored with @asliozyurek.bsky.social, Gerardo Ortega, Kadir Gökgöz, and @esamghaleb.bsky.social arXiv: arxiv.org/abs/2510.08482

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Onur Keleş @onurkeles.bsky.social · 03/05/2025

Can BERT help save endangered languages? Excited to present this paper tomorrow at LM4UC @naaclmeeting.bsky.social! We explored how multilingual BERT with augmented data perform POS tagging & NER for Hamshentsnag #NAACL 🔗 Paper: aclanthology.org/2025.lm4uc-1.9/

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Onur Keleş @onurkeles.bsky.social · 02/05/2025

Are your LLMs good-enough? 🤔 Our new paper w/ @nazik.bsky.social at #CMCL2025 @naaclmeeting.bsky.social shows both humans & smaller LLMs do good-enough parsing in Turkish role-reversal contexts. GPT-2 better predicts human RTs. LLaMA-3 does less heuristic parses but lacks predictive power.

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Onur Keleş @onurkeles.bsky.social · 26/03/2025

I’ll be at #HSP2025 at the University of Maryland @UofMaryland, College Park, to present my MA thesis work on using pose estimation to detect phonetic reduction in Turkish Sign Language from March 27 to 29. Would love to meet and chat if you are in the area!

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Onur Keleş @onurkeles.bsky.social · 11/03/2025

What’s a better first Bluesky post than introduce my recently defended MA thesis? I examined signed narratives with a production experiment and computer vision to answer a simple question: “Do signers display language economy as evidenced by phonetic reduction and referring expression choice?”

Thesis abstract. ABSTRACT
˙
Discourse Cohesion and Phonetics in Turkish Sign Language (T
ID): An
Experimental and Computational Approach
Theories of linguistic efficiency, such as Zipf’s (1949) law, claim that languages
reduce effort by favoring simpler or more economical forms whenever possible.
Although this claim has widely been tested and confirmed in spoken languages (e.g.,
Givon, 1983; Gundel, Hedberg, & Zacharski, 1993), it has not been addressed as
widely in sign languages. This thesis investigates how such theories would be
˙
realized in Turkish Sign Language (T
ID) through discourse cohesion and phonetic
˙
reduction in the narratives of T
ID. With a story-retelling production experiment,
discourse cohesion has been analyzed using a quantized measure of accessibility
adapted from Ariel’s (1990) framework. In particular, I examine referring expression
˙forms (e.g., nominal versus verbal) that native and late-signing deaf adult T
ID signers
employ in narratives. The articulatory phonetic aspect of narratives has been analyzed
using MediaPipe, an open-source computer vision tool. The results of the discourse
cohesion experiment display similarities with previous research on spoken language
and other sign languages. A strong relationship was found between the cognitive
accessibility scores of referents and the discourse context (e.g., first mention,
maintenance, re-introduction), type of the referring expression, and age of acquisition
˙in T
ID. The results of the computational phonetic analysis showed the forms that T
ID
signers used underwent phonetic reduction as the cognitive accessibility of a referent
increased. They had shorter duration, smaller hand movement, and narrower signing
space. Age of acquisition or delayed first acquisition did not significantly affect these
measures except for duration, in which native signers retold the events faster than late
signers.

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