利用 BERT 语言模型改进投票建议应用程序的设计。

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Artificial Intelligence Pub Date : 2024-08-06 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1343214
Daniil Buryakov, Mate Kovacs, Uwe Serdült, Victor Kryssanov
{"title":"利用 BERT 语言模型改进投票建议应用程序的设计。","authors":"Daniil Buryakov, Mate Kovacs, Uwe Serdült, Victor Kryssanov","doi":"10.3389/frai.2024.1343214","DOIUrl":null,"url":null,"abstract":"<p><p>The relevance and importance of voting advice applications (VAAs) are demonstrated by their popularity among potential voters. On average, around 30% of voters take into account the recommendations of these applications during elections. The comparison between potential voters' and parties' positions is made on the basis of VAA policy statements on which users are asked to express opinions. VAA designers devote substantial time and effort to analyzing domestic and international politics to formulate policy statements and select those to be included in the application. This procedure involves manually reading and evaluating a large volume of publicly available data, primarily party manifestos. A problematic part of the work is the limited time frame. This study proposes a system to assist VAA designers in formulating, revising, and selecting policy statements. Using pre-trained language models and machine learning methods to process politics-related textual data, the system produces a set of suggestions corresponding to relevant VAA statements. Experiments were conducted using party manifestos and YouTube comments from Japan, combined with VAA policy statements from six Japanese and two European VAAs. The technical approaches used in the system are based on the BERT language model, which is known for its capability to capture the context of words in the documents. Although the output of the system does not completely eliminate the need for manual human assessment, it provides valuable suggestions for updating VAA policy statements on an objective, i.e., bias-free, basis.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1343214"},"PeriodicalIF":3.0000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333799/pdf/","citationCount":"0","resultStr":"{\"title\":\"Enhancing the design of voting advice applications with BERT language model.\",\"authors\":\"Daniil Buryakov, Mate Kovacs, Uwe Serdült, Victor Kryssanov\",\"doi\":\"10.3389/frai.2024.1343214\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The relevance and importance of voting advice applications (VAAs) are demonstrated by their popularity among potential voters. On average, around 30% of voters take into account the recommendations of these applications during elections. The comparison between potential voters' and parties' positions is made on the basis of VAA policy statements on which users are asked to express opinions. VAA designers devote substantial time and effort to analyzing domestic and international politics to formulate policy statements and select those to be included in the application. This procedure involves manually reading and evaluating a large volume of publicly available data, primarily party manifestos. A problematic part of the work is the limited time frame. This study proposes a system to assist VAA designers in formulating, revising, and selecting policy statements. Using pre-trained language models and machine learning methods to process politics-related textual data, the system produces a set of suggestions corresponding to relevant VAA statements. Experiments were conducted using party manifestos and YouTube comments from Japan, combined with VAA policy statements from six Japanese and two European VAAs. The technical approaches used in the system are based on the BERT language model, which is known for its capability to capture the context of words in the documents. Although the output of the system does not completely eliminate the need for manual human assessment, it provides valuable suggestions for updating VAA policy statements on an objective, i.e., bias-free, basis.</p>\",\"PeriodicalId\":33315,\"journal\":{\"name\":\"Frontiers in Artificial Intelligence\",\"volume\":\"7 \",\"pages\":\"1343214\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333799/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/frai.2024.1343214\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frai.2024.1343214","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

投票建议应用程序(VAA)在潜在选民中的受欢迎程度证明了其相关性和重要性。在选举期间,平均约有 30% 的选民会考虑这些应用程序的建议。潜在选民和政党立场之间的比较是在 VAA 政策声明的基础上进行的,用户需要就这些政策声明发表意见。VAA 设计人员花费大量时间和精力分析国内和国际政治,以制定政策声明并选择纳入应用程序的政策声明。这一过程需要人工阅读和评估大量公开数据,主要是政党宣言。这项工作的一个问题是时间有限。本研究提出了一个系统来协助 VAA 设计人员制定、修改和选择政策声明。该系统使用预先训练好的语言模型和机器学习方法来处理与政治相关的文本数据,并提出一系列与相关自愿性评估声明相对应的建议。实验使用了日本的政党宣言和 YouTube 评论,以及六个日本和两个欧洲 VAA 的 VAA 政策声明。系统中使用的技术方法基于 BERT 语言模型,该模型以能够捕捉文档中单词的上下文而著称。虽然该系统的输出结果并不能完全消除人工评估的需要,但它为在客观(即无偏见)的基础上更新 VAA 政策声明提供了宝贵的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing the design of voting advice applications with BERT language model.

The relevance and importance of voting advice applications (VAAs) are demonstrated by their popularity among potential voters. On average, around 30% of voters take into account the recommendations of these applications during elections. The comparison between potential voters' and parties' positions is made on the basis of VAA policy statements on which users are asked to express opinions. VAA designers devote substantial time and effort to analyzing domestic and international politics to formulate policy statements and select those to be included in the application. This procedure involves manually reading and evaluating a large volume of publicly available data, primarily party manifestos. A problematic part of the work is the limited time frame. This study proposes a system to assist VAA designers in formulating, revising, and selecting policy statements. Using pre-trained language models and machine learning methods to process politics-related textual data, the system produces a set of suggestions corresponding to relevant VAA statements. Experiments were conducted using party manifestos and YouTube comments from Japan, combined with VAA policy statements from six Japanese and two European VAAs. The technical approaches used in the system are based on the BERT language model, which is known for its capability to capture the context of words in the documents. Although the output of the system does not completely eliminate the need for manual human assessment, it provides valuable suggestions for updating VAA policy statements on an objective, i.e., bias-free, basis.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信