--- title: "Junyi Jessy Li — papers and cards" description: "Junyi Jessy Li: 5 paper cards in the Research library (2020–2025), with what each paper is about, the ideas Mova took from it, and the co-authors." canonical: "https://research.elamur.ai/free/en/authors/junyi-jessy-li/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/junyi-jessy-li/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Junyi Jessy Li # Junyi Jessy Li 5 paper cards in the Research library · 2020–2025 Junyi Jessy Li wrote 5 of the papers Mova reads, in 2020–2025, most of them on [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/), [Dependency parsing](https://md.research.elamur.ai/free/en/topics/dependency-parsing/); often with [Greg Durrett](https://md.research.elamur.ai/free/en/authors/greg-durrett/), [Yating Wu](https://md.research.elamur.ai/free/en/authors/yating-wu/), [Kyle Mahowald](https://md.research.elamur.ai/free/en/authors/kyle-mahowald/). [OpenAlex](https://openalex.org/A5021057186) · [ORCID](https://orcid.org/0000-0002-2550-5262) · [**h-index 22**](https://openalex.org/authors/A5021057186) · [1801 citations](https://openalex.org/authors/A5021057186) · [108 works](https://openalex.org/works?filter=author.id:A5021057186) — openalex-snapshot, 2026-10-14 The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Junyi+Jessy+Li) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Junyi+Jessy+Li) · [dblp](https://dblp.org/search?q=Junyi+Jessy+Li) · [ACL Anthology](https://aclanthology.org/search/?q=Junyi+Jessy+Li) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Junyi+Jessy+Li) Topics: [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) 3 · [Dependency parsing](https://md.research.elamur.ai/free/en/topics/dependency-parsing/) 1 ## Papers 1. **[Is It JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs](https://md.research.elamur.ai/free/en/papers/sheffield-2025-is-it-textit-just-semantics/)** *(2025)* The English discourse particle "just" has several senses: exclusive ("Betsy just eats chicken nuggets"), temporal ("just arrived"), unelaboratory ("Fido is just a dog"), unexplanatory ("the lights just turn on and off") and emphatic ("just delicious"). 2. **[Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion](https://md.research.elamur.ai/free/en/papers/ko-2023-discourse-analysis-questions-answers-parsing/)** *(2023)* The paper builds the first parser for QUD dependency structures: every sentence after the first gets an anchor sentence earlier in the document and a free-form question, raised by the anchor, that the sentence answers. 3. **[Elaborative Simplification as Implicit Questions Under Discussion](https://md.research.elamur.ai/free/en/papers/wu-2023-elaborative-simplification-as-implicit-questions/)** *(2023)* When editors simplify news for children (Newsela), they often add sentences that explain rather than shorten. 4. **[QUDeval: The Evaluation of Questions Under Discussion Discourse Parsing](https://md.research.elamur.ai/free/en/papers/wu-2023-qudeval-evaluation-questions-under-discussion/)** *(2023)* A QUD dependency parse links each answer sentence to an earlier anchor sentence by an implicit question; evaluating such free-form questions has been manual. 5. **[Inquisitive Question Generation for High Level Text Comprehension](https://md.research.elamur.ai/free/en/papers/ko-2020-inquisitive-question-generation-high-level/)** *(2020)* INQUISITIVE is a set of about 19K questions that crowd workers asked while reading the first five sentences of 1,500 news articles one sentence at a time, each question tied to the span it is about and checked not to be answered already by the earlier text. ## Co-authors (13) - [Greg Durrett](https://md.research.elamur.ai/free/en/authors/greg-durrett/) ×3 - [Yating Wu](https://md.research.elamur.ai/free/en/authors/yating-wu/) ×3 - [Kyle Mahowald](https://md.research.elamur.ai/free/en/authors/kyle-mahowald/) ×2 - [Wei-Jen Ko](https://md.research.elamur.ai/free/en/authors/wei-jen-ko/) ×2 - [William Sheffield](https://md.research.elamur.ai/free/en/authors/william-sheffield/) ×2 - Ashwini Deo - Cutter Dalton - Dananjay Srinivas - Kanishka Misra - Ritika Mangla - Te-yuan Chen - [Valentina Pyatkin](https://md.research.elamur.ai/free/en/authors/valentina-pyatkin/) - Yiyan Huang ## More papers in the library (6) Pages of the Research library: our summary, the ideas, references and who cites each. - [Paragraph-level Simplification of Medical Texts](https://md.research.elamur.ai/free/en/papers/devaraj-2021-paragraph-level-simplification-medical-texts/) — Ashwin Devaraj, Iain Marshall, Byron Wallace, et al. · 2021 - [Where Are We in Discourse Relation Recognition?](https://md.research.elamur.ai/free/en/papers/atwell-2021-where-are-we-discourse-relation/) — Katherine Atwell, Junyi Jessy Li, Malihe Alikhani · 2021 - [Assessing Discourse Relations in Language Generation from GPT-2](https://md.research.elamur.ai/free/en/papers/ko-2020-assessing-discourse-relations-language-generation/) — Wei-Jen Ko, Junyi Jessy Li · 2020 - [A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature](https://md.research.elamur.ai/free/en/papers/nye-2018-corpus-multi-level-annotations-patients/) — Benjamin Nye, Junyi Jessy Li, Roma Patel, et al. · 2018 - [Cross-lingual Discourse Relation Analysis: A corpus study and a semi-supervised classification system](https://md.research.elamur.ai/free/en/papers/li-2014-cross-lingual-discourse-relation-analysis/) — Junyi Jessy Li, Marine Carpuat, Ani Nenkova · 2014 - [Reducing Sparsity Improves the Recognition of Implicit Discourse Relations](https://md.research.elamur.ai/free/en/papers/li-2014-reducing-sparsity-improves-recognition-implicit/) — Junyi Jessy Li, Ani Nenkova · 2014