@inproceedings{yang-etal-2020-textlearner,
title = "{T}ext{L}earner at {S}em{E}val-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text",
author = "Yang, Zhishen and
Wolfsteller, Lars and
Okazaki, Naoaki",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.221/",
doi = "10.18653/v1/2020.semeval-1.221",
pages = "1691--1697",
abstract = "This paper describes the emphasis selection system of the team TextLearner for SemEval 2020 Task 10: Emphasis Selection For Written Text in Visual Media. The system aims to learn the emphasis selection distribution using contextual representations extracted from pre-trained language models and a two-staged ranking model. The experimental results demonstrate the strong contextual representation power of the recent advanced transformer-based language model RoBERTa, which can be exploited using a simple but effective architecture on top."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yang-etal-2020-textlearner">
<titleInfo>
<title>TextLearner at SemEval-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text</title>
</titleInfo>
<name type="personal">
<namePart type="given">Zhishen</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lars</namePart>
<namePart type="family">Wolfsteller</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Naoaki</namePart>
<namePart type="family">Okazaki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourteenth Workshop on Semantic Evaluation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Aurelie</namePart>
<namePart type="family">Herbelot</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaodan</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexis</namePart>
<namePart type="family">Palmer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathan</namePart>
<namePart type="family">Schneider</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jonathan</namePart>
<namePart type="family">May</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Shutova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>International Committee for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Barcelona (online)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper describes the emphasis selection system of the team TextLearner for SemEval 2020 Task 10: Emphasis Selection For Written Text in Visual Media. The system aims to learn the emphasis selection distribution using contextual representations extracted from pre-trained language models and a two-staged ranking model. The experimental results demonstrate the strong contextual representation power of the recent advanced transformer-based language model RoBERTa, which can be exploited using a simple but effective architecture on top.</abstract>
<identifier type="citekey">yang-etal-2020-textlearner</identifier>
<identifier type="doi">10.18653/v1/2020.semeval-1.221</identifier>
<location>
<url>https://aclanthology.org/2020.semeval-1.221/</url>
</location>
<part>
<date>2020-12</date>
<extent unit="page">
<start>1691</start>
<end>1697</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T TextLearner at SemEval-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text
%A Yang, Zhishen
%A Wolfsteller, Lars
%A Okazaki, Naoaki
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F yang-etal-2020-textlearner
%X This paper describes the emphasis selection system of the team TextLearner for SemEval 2020 Task 10: Emphasis Selection For Written Text in Visual Media. The system aims to learn the emphasis selection distribution using contextual representations extracted from pre-trained language models and a two-staged ranking model. The experimental results demonstrate the strong contextual representation power of the recent advanced transformer-based language model RoBERTa, which can be exploited using a simple but effective architecture on top.
%R 10.18653/v1/2020.semeval-1.221
%U https://aclanthology.org/2020.semeval-1.221/
%U https://doi.org/10.18653/v1/2020.semeval-1.221
%P 1691-1697
Markdown (Informal)
[TextLearner at SemEval-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text](https://aclanthology.org/2020.semeval-1.221/) (Yang et al., SemEval 2020)
ACL