---
title: "ParseJargon — paper by Song et al. (2026) — summary and ideas"
description: "People in cross-disciplinary meetings often meet unfamiliar technical terms, and they seldom ask for clarification because… 42 references."
authors: ["Yifan Song", "Wing Yee Au", "Hon Yung Wong", "Brian Bailey", "Tal August"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)"
source: "https://aclanthology.org/2026.acl-demo.61/"
doi: "10.18653/v1/2026.acl-demo.61"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/song-2026-parsejargon-personalized-real-time-jargon/"
---
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# ParseJargon: Personalized Real-time Jargon Support in Online Meetings

**Authors:** [Yifan Song](https://md.research.elamur.ai/free/en/authors/yifan-song/), Wing Yee Au, Hon Yung Wong, [Brian Bailey](https://md.research.elamur.ai/free/en/authors/brian-bailey/) and [Tal August](https://md.research.elamur.ai/free/en/authors/tal-august/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-demo.61/) · [PDF](https://aclanthology.org/2026.acl-demo.61.pdf) · [DOI 10.18653/v1/2026.acl-demo.61](https://doi.org/10.18653/v1/2026.acl-demo.61) · [OpenAlex](https://openalex.org/W7166809316)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
People in cross-disciplinary meetings often meet unfamiliar technical terms, and they seldom ask for clarification because doing so is distracting or socially awkward. ParseJargon is an LLM-based assistant that tackles this live. It reads the meeting platform's captions and uses GPT prompting in two stages. First, it detects jargon and writes short plain-language definitions. Second, it removes terms the listener probably already knows, based on a single-sentence self-description of their education or role. Definitions appear in a sidebar next to highlighted captions and a running glossary.

The authors ran a within-subjects study in which seven colleagues watched each other's presentations under three conditions: no support, generic support and personalized support. Generic glossaries helped understanding but swamped viewers with too many terms. Personalized support raised comprehension, kept engagement up and increased how much participants valued their peers' work. A latency test showed definitions arrive well before the relevant caption leaves the screen. The work suggests that even minimal user profiling can make real-time language assistance precise enough to be useful.

## Key points
- **Two-stage pipeline:** first identify and explain jargon, then filter by a one-sentence profile. The tool runs on gpt-4o and is built as a Chrome extension for Zoom Web.
- **Study design:** seven participants from one technology company, giving 42 participant–presentation pairs (14 per condition).
- **Precision gain:** personalized glossaries listed far fewer terms on average (9.71 vs. 22.57). Identification precision rose from 47.03% to 77.51%.
- **Latency:** backend median/95th-percentile latency was 0.32/1.36 s for generic support and 0.42/1.73 s for personalized support. Caption chunks stayed on screen for a median of 4.15 s.
- **Limitations:** the sample is small and comes from a single company. Filtering is binary, and the system depends on external speech-to-text and LLM services. Other models and richer profiles were not benchmarked.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Illinois Urbana-Champaign](https://md.research.elamur.ai/free/en/universities/university-of-illinois-urbana-champaign/) — University, Urbana, United States · 184 works · 69,372 citations

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## Cite

Cite as: Yifan Song, Wing Yee Au, Hon Yung Wong, Brian Bailey and Tal August. 2026. ParseJargon: Personalized Real-time Jargon Support in Online Meetings. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{song2026parsejargon,
  title = {{ParseJargon: Personalized Real-time Jargon Support in Online Meetings}},
  author = {Yifan Song, Wing Yee Au, Hon Yung Wong, Brian Bailey and Tal August},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-demo.61},
  url = {https://doi.org/10.18653/v1/2026.acl-demo.61}
}
```

- [← previous ParsCORE: The Persian Corpus of Online Registers](https://md.research.elamur.ai/free/en/papers/razzaghi-2026-parscore-persian-corpus-online-registers/)
- [next → Parsing Arabic Dialects Revisited: New Benchmarks, Models, and Insights](https://md.research.elamur.ai/free/en/papers/elshabrawy-2026-parsing-arabic-dialects-revisited-new/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/39/)

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*Updated 1 November 2026*