基于对抗性输入的自动作文评分预训练语言模型的比较研究

Phakawat Wangkriangkri, Chanissara Viboonlarp, Attapol T. Rutherford, E. Chuangsuwanich
{"title":"基于对抗性输入的自动作文评分预训练语言模型的比较研究","authors":"Phakawat Wangkriangkri, Chanissara Viboonlarp, Attapol T. Rutherford, E. Chuangsuwanich","doi":"10.1109/TENCON50793.2020.9293930","DOIUrl":null,"url":null,"abstract":"Automated Essay Scoring (AES) is a task that deals with grading written essays automatically without human intervention. This study compares the performance of three AES models which utilize different text embedding methods, namely Global Vectors for Word Representation (GloVe), Embeddings from Language Models (ELMo), and Bidirectional Encoder Representations from Transformers (BERT). We used two evaluation metrics: Quadratic Weighted Kappa (QWK) and a novel \"robustness\", which quantifies the models’ ability to detect adversarial essays created by modifying normal essays to cause them to be less coherent. We found that: (1) the BERT-based model achieved the greatest robustness, followed by the GloVe-based and ELMo-based models, respectively, and (2) fine-tuning the embeddings improves QWK but lowers robustness. These findings could be informative on how to choose, and whether to fine-tune, an appropriate model based on how much the AES program places emphasis on proper grading of adversarial essays.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A Comparative Study of Pretrained Language Models for Automated Essay Scoring with Adversarial Inputs\",\"authors\":\"Phakawat Wangkriangkri, Chanissara Viboonlarp, Attapol T. Rutherford, E. Chuangsuwanich\",\"doi\":\"10.1109/TENCON50793.2020.9293930\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automated Essay Scoring (AES) is a task that deals with grading written essays automatically without human intervention. This study compares the performance of three AES models which utilize different text embedding methods, namely Global Vectors for Word Representation (GloVe), Embeddings from Language Models (ELMo), and Bidirectional Encoder Representations from Transformers (BERT). We used two evaluation metrics: Quadratic Weighted Kappa (QWK) and a novel \\\"robustness\\\", which quantifies the models’ ability to detect adversarial essays created by modifying normal essays to cause them to be less coherent. We found that: (1) the BERT-based model achieved the greatest robustness, followed by the GloVe-based and ELMo-based models, respectively, and (2) fine-tuning the embeddings improves QWK but lowers robustness. These findings could be informative on how to choose, and whether to fine-tune, an appropriate model based on how much the AES program places emphasis on proper grading of adversarial essays.\",\"PeriodicalId\":283131,\"journal\":{\"name\":\"2020 IEEE REGION 10 CONFERENCE (TENCON)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE REGION 10 CONFERENCE (TENCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TENCON50793.2020.9293930\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE REGION 10 CONFERENCE (TENCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TENCON50793.2020.9293930","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

自动论文评分(AES)是一项在没有人为干预的情况下自动对书面论文进行评分的任务。本研究比较了使用不同文本嵌入方法的三种AES模型的性能,即全局向量词表示(GloVe)、语言模型嵌入(ELMo)和变形器双向编码器表示(BERT)。我们使用了两种评估指标:二次加权Kappa (QWK)和一种新的“鲁棒性”,它量化了模型检测对抗性文章的能力,这种对抗性文章是通过修改正常文章而产生的,从而使它们不那么连贯。我们发现:(1)基于bert的模型鲁棒性最强,其次是基于glove和elmo的模型;(2)微调嵌入提高了QWK,但降低了鲁棒性。这些发现可以为如何选择以及是否微调一个适当的模型提供信息,该模型基于AES程序在多大程度上强调对抗性论文的适当评分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comparative Study of Pretrained Language Models for Automated Essay Scoring with Adversarial Inputs
Automated Essay Scoring (AES) is a task that deals with grading written essays automatically without human intervention. This study compares the performance of three AES models which utilize different text embedding methods, namely Global Vectors for Word Representation (GloVe), Embeddings from Language Models (ELMo), and Bidirectional Encoder Representations from Transformers (BERT). We used two evaluation metrics: Quadratic Weighted Kappa (QWK) and a novel "robustness", which quantifies the models’ ability to detect adversarial essays created by modifying normal essays to cause them to be less coherent. We found that: (1) the BERT-based model achieved the greatest robustness, followed by the GloVe-based and ELMo-based models, respectively, and (2) fine-tuning the embeddings improves QWK but lowers robustness. These findings could be informative on how to choose, and whether to fine-tune, an appropriate model based on how much the AES program places emphasis on proper grading of adversarial essays.
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
自引率
0.00%
发文量
0
×
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