@inproceedings{ju-etal-2025-dedisco,
title = "{D}e{D}is{C}o at the {DISRPT} 2025 Shared Task: A System for Discourse Relation Classification",
author = "Ju, Zhuoxuan and
Wu, Jingni and
Purushothama, Abhishek and
Zeldes, Amir",
editor = "Braud, Chlo{\'e} and
Liu, Yang Janet and
Muller, Philippe and
Zeldes, Amir and
Li, Chuyuan",
booktitle = "Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025)",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.disrpt-1.4/",
pages = "48--62",
ISBN = "979-8-89176-344-9",
abstract = "This paper presents DeDisCo, Georgetown University{'}s entry in the DISRPT 2025 shared task on discourse relation classification. We test two approaches, using an mt5-based encoder and a decoder based approach using the openly available Qwen model. We also experiment on training with augmented dataset for low-resource languages using matched data translated automatically from English, as well as using some additional linguistic features inspired by entries in previous editions of the Shared Task. Our system achieves a macro-accuracy score of 71.28, and we provide some interpretation and error analysis for our results."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ju-etal-2025-dedisco">
<titleInfo>
<title>DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification</title>
</titleInfo>
<name type="personal">
<namePart type="given">Zhuoxuan</namePart>
<namePart type="family">Ju</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jingni</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abhishek</namePart>
<namePart type="family">Purushothama</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Amir</namePart>
<namePart type="family">Zeldes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chloé</namePart>
<namePart type="family">Braud</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yang</namePart>
<namePart type="given">Janet</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Philippe</namePart>
<namePart type="family">Muller</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Amir</namePart>
<namePart type="family">Zeldes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chuyuan</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Suzhou, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-344-9</identifier>
</relatedItem>
<abstract>This paper presents DeDisCo, Georgetown University’s entry in the DISRPT 2025 shared task on discourse relation classification. We test two approaches, using an mt5-based encoder and a decoder based approach using the openly available Qwen model. We also experiment on training with augmented dataset for low-resource languages using matched data translated automatically from English, as well as using some additional linguistic features inspired by entries in previous editions of the Shared Task. Our system achieves a macro-accuracy score of 71.28, and we provide some interpretation and error analysis for our results.</abstract>
<identifier type="citekey">ju-etal-2025-dedisco</identifier>
<location>
<url>https://aclanthology.org/2025.disrpt-1.4/</url>
</location>
<part>
<date>2025-11</date>
<extent unit="page">
<start>48</start>
<end>62</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification
%A Ju, Zhuoxuan
%A Wu, Jingni
%A Purushothama, Abhishek
%A Zeldes, Amir
%Y Braud, Chloé
%Y Liu, Yang Janet
%Y Muller, Philippe
%Y Zeldes, Amir
%Y Li, Chuyuan
%S Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-344-9
%F ju-etal-2025-dedisco
%X This paper presents DeDisCo, Georgetown University’s entry in the DISRPT 2025 shared task on discourse relation classification. We test two approaches, using an mt5-based encoder and a decoder based approach using the openly available Qwen model. We also experiment on training with augmented dataset for low-resource languages using matched data translated automatically from English, as well as using some additional linguistic features inspired by entries in previous editions of the Shared Task. Our system achieves a macro-accuracy score of 71.28, and we provide some interpretation and error analysis for our results.
%U https://aclanthology.org/2025.disrpt-1.4/
%P 48-62
Markdown (Informal)
[DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification](https://aclanthology.org/2025.disrpt-1.4/) (Ju et al., DISRPT 2025)
ACL