@inproceedings{kan-etal-2021-home,
title = "Home Appliance Review Research Via Adversarial Reptile",
author = "Kan, Tai-Jung and
Chang, Chia-Hui and
Chuang, Hsiu-Min",
editor = "Lee, Lung-Hao and
Chang, Chia-Hui and
Chen, Kuan-Yu",
booktitle = "Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)",
month = oct,
year = "2021",
address = "Taoyuan, Taiwan",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
url = "https://aclanthology.org/2021.rocling-1.24",
pages = "183--191",
abstract = "For manufacturers of home appliances, the Studying discussion of products on social media can help manufacturers improve their products. Opinions provided through online reviews can immediately reflect whether the product is accepted by people, and which aspect of the product are most discussed . In this article, we divide the analysis of home appliances into three tasks, including named entity recognition (NER), aspect category extraction (ACE), and aspect category sentiment classification (ACSC). To improve the performance of ACSC, we combine the Reptile algorithm in meta learning with the concept of domain adversarial training to form the concept of the Adversarial Reptile algorithm. We find show that the macro-f1 is improved from 68.6{\%} (BERT fine tuned model) to 70.3{\%} (p-value 0.04).",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kan-etal-2021-home">
<titleInfo>
<title>Home Appliance Review Research Via Adversarial Reptile</title>
</titleInfo>
<name type="personal">
<namePart type="given">Tai-Jung</namePart>
<namePart type="family">Kan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chia-Hui</namePart>
<namePart type="family">Chang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hsiu-Min</namePart>
<namePart type="family">Chuang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Lung-Hao</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chia-Hui</namePart>
<namePart type="family">Chang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kuan-Yu</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)</publisher>
<place>
<placeTerm type="text">Taoyuan, Taiwan</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>For manufacturers of home appliances, the Studying discussion of products on social media can help manufacturers improve their products. Opinions provided through online reviews can immediately reflect whether the product is accepted by people, and which aspect of the product are most discussed . In this article, we divide the analysis of home appliances into three tasks, including named entity recognition (NER), aspect category extraction (ACE), and aspect category sentiment classification (ACSC). To improve the performance of ACSC, we combine the Reptile algorithm in meta learning with the concept of domain adversarial training to form the concept of the Adversarial Reptile algorithm. We find show that the macro-f1 is improved from 68.6% (BERT fine tuned model) to 70.3% (p-value 0.04).</abstract>
<identifier type="citekey">kan-etal-2021-home</identifier>
<location>
<url>https://aclanthology.org/2021.rocling-1.24</url>
</location>
<part>
<date>2021-10</date>
<extent unit="page">
<start>183</start>
<end>191</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Home Appliance Review Research Via Adversarial Reptile
%A Kan, Tai-Jung
%A Chang, Chia-Hui
%A Chuang, Hsiu-Min
%Y Lee, Lung-Hao
%Y Chang, Chia-Hui
%Y Chen, Kuan-Yu
%S Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
%D 2021
%8 October
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taoyuan, Taiwan
%F kan-etal-2021-home
%X For manufacturers of home appliances, the Studying discussion of products on social media can help manufacturers improve their products. Opinions provided through online reviews can immediately reflect whether the product is accepted by people, and which aspect of the product are most discussed . In this article, we divide the analysis of home appliances into three tasks, including named entity recognition (NER), aspect category extraction (ACE), and aspect category sentiment classification (ACSC). To improve the performance of ACSC, we combine the Reptile algorithm in meta learning with the concept of domain adversarial training to form the concept of the Adversarial Reptile algorithm. We find show that the macro-f1 is improved from 68.6% (BERT fine tuned model) to 70.3% (p-value 0.04).
%U https://aclanthology.org/2021.rocling-1.24
%P 183-191
Markdown (Informal)
[Home Appliance Review Research Via Adversarial Reptile](https://aclanthology.org/2021.rocling-1.24) (Kan et al., ROCLING 2021)
ACL
- Tai-Jung Kan, Chia-Hui Chang, and Hsiu-Min Chuang. 2021. Home Appliance Review Research Via Adversarial Reptile. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 183–191, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).