@inproceedings{yu-etal-2019-gumdrop,
title = "{G}um{D}rop at the {DISRPT}2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection",
author = "Yu, Yue and
Zhu, Yilun and
Liu, Yang and
Liu, Yan and
Peng, Siyao and
Gong, Mackenzie and
Zeldes, Amir",
editor = "Zeldes, Amir and
Das, Debopam and
Galani, Erick Maziero and
Antonio, Juliano Desiderato and
Iruskieta, Mikel",
booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
month = jun,
year = "2019",
address = "Minneapolis, MN",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2717",
doi = "10.18653/v1/W19-2717",
pages = "133--143",
abstract = "In this paper we present GumDrop, Georgetown University{'}s entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yu-etal-2019-gumdrop">
<titleInfo>
<title>GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yue</namePart>
<namePart type="family">Yu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yilun</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yang</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yan</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Siyao</namePart>
<namePart type="family">Peng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mackenzie</namePart>
<namePart type="family">Gong</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>2019-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019</title>
</titleInfo>
<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">Debopam</namePart>
<namePart type="family">Das</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Erick</namePart>
<namePart type="given">Maziero</namePart>
<namePart type="family">Galani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Juliano</namePart>
<namePart type="given">Desiderato</namePart>
<namePart type="family">Antonio</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mikel</namePart>
<namePart type="family">Iruskieta</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Minneapolis, MN</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this paper we present GumDrop, Georgetown University’s entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.</abstract>
<identifier type="citekey">yu-etal-2019-gumdrop</identifier>
<identifier type="doi">10.18653/v1/W19-2717</identifier>
<location>
<url>https://aclanthology.org/W19-2717</url>
</location>
<part>
<date>2019-06</date>
<extent unit="page">
<start>133</start>
<end>143</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection
%A Yu, Yue
%A Zhu, Yilun
%A Liu, Yang
%A Liu, Yan
%A Peng, Siyao
%A Gong, Mackenzie
%A Zeldes, Amir
%Y Zeldes, Amir
%Y Das, Debopam
%Y Galani, Erick Maziero
%Y Antonio, Juliano Desiderato
%Y Iruskieta, Mikel
%S Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, MN
%F yu-etal-2019-gumdrop
%X In this paper we present GumDrop, Georgetown University’s entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.
%R 10.18653/v1/W19-2717
%U https://aclanthology.org/W19-2717
%U https://doi.org/10.18653/v1/W19-2717
%P 133-143
Markdown (Informal)
[GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection](https://aclanthology.org/W19-2717) (Yu et al., NAACL 2019)
ACL