@inproceedings{morio-etal-2020-hitachi,
title = "Hitachi at {S}em{E}val-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models",
author = "Morio, Gaku and
Morishita, Terufumi and
Ozaki, Hiroaki and
Miyoshi, Toshinori",
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-1.216/",
doi = "10.18653/v1/2020.semeval-1.216",
pages = "1658--1664",
abstract = "This paper shows our system for SemEval-2020 task 10, Emphasis Selection for Written Text in Visual Media. Our strategy is two-fold. First, we propose fine-tuning many pre-trained language models, predicting an emphasis probability distribution over tokens. Then, we propose stacking a trainable distribution fusion DistFuse system to fuse the predictions of the fine-tuned models. Experimental results show tha DistFuse is comparable or better when compared with a naive average ensemble. As a result, we were ranked 2nd amongst 31 teams."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="morio-etal-2020-hitachi">
<titleInfo>
<title>Hitachi at SemEval-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Gaku</namePart>
<namePart type="family">Morio</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Terufumi</namePart>
<namePart type="family">Morishita</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hiroaki</namePart>
<namePart type="family">Ozaki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Toshinori</namePart>
<namePart type="family">Miyoshi</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 shows our system for SemEval-2020 task 10, Emphasis Selection for Written Text in Visual Media. Our strategy is two-fold. First, we propose fine-tuning many pre-trained language models, predicting an emphasis probability distribution over tokens. Then, we propose stacking a trainable distribution fusion DistFuse system to fuse the predictions of the fine-tuned models. Experimental results show tha DistFuse is comparable or better when compared with a naive average ensemble. As a result, we were ranked 2nd amongst 31 teams.</abstract>
<identifier type="citekey">morio-etal-2020-hitachi</identifier>
<identifier type="doi">10.18653/v1/2020.semeval-1.216</identifier>
<location>
<url>https://aclanthology.org/2020.semeval-1.216/</url>
</location>
<part>
<date>2020-12</date>
<extent unit="page">
<start>1658</start>
<end>1664</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Hitachi at SemEval-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models
%A Morio, Gaku
%A Morishita, Terufumi
%A Ozaki, Hiroaki
%A Miyoshi, Toshinori
%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 morio-etal-2020-hitachi
%X This paper shows our system for SemEval-2020 task 10, Emphasis Selection for Written Text in Visual Media. Our strategy is two-fold. First, we propose fine-tuning many pre-trained language models, predicting an emphasis probability distribution over tokens. Then, we propose stacking a trainable distribution fusion DistFuse system to fuse the predictions of the fine-tuned models. Experimental results show tha DistFuse is comparable or better when compared with a naive average ensemble. As a result, we were ranked 2nd amongst 31 teams.
%R 10.18653/v1/2020.semeval-1.216
%U https://aclanthology.org/2020.semeval-1.216/
%U https://doi.org/10.18653/v1/2020.semeval-1.216
%P 1658-1664
Markdown (Informal)
[Hitachi at SemEval-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models](https://aclanthology.org/2020.semeval-1.216/) (Morio et al., SemEval 2020)
ACL