# How Pragmatics Shape Articulation: A Computational Case Study in STEM ASL Discourse

arXiv: https://arxiv.org/abs/2510.23842 · HTML: https://mlsanity.com/paper/2510.23842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 61 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020)
- [MediaPipe Hands: On-device Real-time Hand Tracking](https://mlsanity.com/paper/2006.10214/) (2020)
- [BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues](https://mlsanity.com/paper/2007.12131/) (2020)
- [Read and Attend: Temporal Localisation in Sign Language Videos](https://mlsanity.com/paper/2103.16481/) (2021)
- [SignCLIP: Connecting Text and Sign Language by Contrastive Learning](https://mlsanity.com/paper/2407.01264/) (2024)
- [Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas](https://mlsanity.com/paper/2403.02563/) (2024)
- [The Sem-Lex Benchmark: Modeling ASL Signs and Their Phonemes](https://mlsanity.com/paper/2310.00196/) (2023)
- [ASL STEM Wiki: Dataset and Benchmark for Interpreting STEM Articles](https://mlsanity.com/paper/2411.05783/) (2024)
- [American Sign Language Handshapes Reflect Pressures for Communicative Efficiency](https://mlsanity.com/paper/2406.04024/) (2024)

## Built on by (most cited first, 1 of 1)

- [Targeted Linguistic Analysis of Sign Language Models with Minimal Translation Pairs](https://mlsanity.com/paper/2604.27232/) (2026)

## Abstract (arXiv)

Most state-of-the-art sign language models are trained on interpreter or isolated vocabulary data, which overlooks the variability that characterizes natural dialogue. However, human communication dynamically adapts to contexts and interlocutors through spatiotemporal changes and articulation style. This specifically manifests itself in educational settings, where novel vocabularies are used by teachers, and students. To address this gap, we collect a motion capture dataset of American Sign Language (ASL) STEM (Science, Technology, Engineering, and Mathematics) dialogue that enables quantitative comparison between dyadic interactive signing, solo signed lecture, and interpreted articles. Using continuous kinematic features, we disentangle dialogue-specific entrainment from individual effort reduction and show spatiotemporal changes across repeated mentions of STEM terms. On average, dialogue signs are 24.6%-44.6% shorter in duration than the isolated signs, and show significant reductions absent in monologue contexts. Finally, we evaluate sign embedding models on their ability to recognize STEM signs and approximate how entrained the participants become over time. Our study bridges linguistic analysis and computational modeling to understand how pragmatics shape sign articulation and its representation in sign language technologies.