Papers by Jayeol Chun
Modal Dependency Parsing as Structured Prediction over Source-Cue Scope (2026.acl-long)
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| Challenge: | Existing work on identifying sources only focuses on defining source-introducing cues . a structured model focuses learning at the source-cue level and constrains event-level decisions to a small, scope-defined candidate set. |
| Approach: | They propose a framework that leverages large language models to explicitly identify source-cue pairs and their respective scope to define modal contexts. |
| Outcome: | The proposed framework surpasses state-of-the-art results by 3 and 4% for English and Chinese datasets. |
Building a Broad Infrastructure for Uniform Meaning Representations (2024.lrec-main)
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Julia Bonn, Matthew J. Buchholz, Jayeol Chun, Andrew Cowell, William Croft, Lukas Denk, Sijia Ge, Jan Hajič, Kenneth Lai, James H. Martin, Skatje Myers, Alexis Palmer, Martha Palmer, Claire Benet Post, James Pustejovsky, Kristine Stenzel, Haibo Sun, Zdeňka Urešová, Rosa Vallejos, Jens E. L. Van Gysel, Meagan Vigus, Nianwen Xue, Jin Zhao
| Challenge: | This paper reports the first release of the UMR data set for six languages . it includes annotations for six different languages that vary greatly in terms of their linguistic properties and resource availability. |
| Approach: | They report the first release of the UMR data set for six languages . they describe on-going efforts to enlarge the data set and extend it to other languages - including Navajo, Navájo, and Sanapaná . |
| Outcome: | The first release of the UMR data set includes annotations for six languages . the language dataset is available for free and can be extended to other languages if needed . |
Modal Dependency Parsing via Biaffine Attention with Self-Loop (2025.findings-acl)
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| Challenge: | A modal dependency structure is a web of connections between events and sources of information in a document that allows for tracing of who-said-what with what levels of certainty. |
| Approach: | They propose a modal dependency structure that integrates biaffine attention with a large language model to optimize against domain-specific challenges of modal dependence parsing. |
| Outcome: | The proposed approach outperforms the previous state-of-the-art on English and Chinese datasets by 2% and 4% respectively. |
AnCast++: Document-Level Evaluation of Graph-based Meaning Representations (2025.findings-acl)
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| Challenge: | Abstract Meaning Representation (UMR) is a cross-lingual document-level graph-based representation that extends it to document- level semantic annotations. |
| Approach: | They propose an evaluation metric that unifies evaluation of four distinct sub-structures of UMR. |
| Outcome: | The proposed metric is made available on Github. |
Building Universal Dependency Treebanks in Korean (L18-1)
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| Challenge: | Several treebanks were introduced for Korean, all of which comprised annotation of morphemes and phrase structure trees, each following its own set of guidelines. |
| Approach: | They propose to use Korean treebanks as dependency trees and to analyze their performance using morpheme-level annotations. |
| Outcome: | The Korean portion of the Google UD Treebank, the Penn Korean Treebank and the KAIST Treebank are re-tokenized and assessed for errors. |