{"created":"2025-01-16T01:46:47.484418+00:00","id":2012336,"links":{},"metadata":{"_buckets":{"deposit":"593d361f-5b1d-4529-a019-f4f30558853a"},"_deposit":{"created_by":7,"id":"2012336","owners":[7],"pid":{"revision_id":0,"type":"depid","value":"2012336"},"status":"published"},"_oai":{"id":"oai:tokushima-u.repo.nii.ac.jp:02012336","sets":["1713853213384:1713853295607"]},"author_link":["769","401"],"control_number":"2012336","item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2024-05-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"9","bibliographicPageStart":"1746","bibliographicVolumeNumber":"13","bibliographic_titles":[{"bibliographic_title":"Electronics","bibliographic_titleLang":"en"}]}]},"item_10001_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"The field of argument analysis has become a crucial component in the advancement of natural language processing, which holds the potential to reveal unprecedented insights from complex data and enable more efficient, cost-effective solutions for enhancing human initiatives. Despite its importance, current technologies face significant challenges, including (1) low interpretability, (2) lack of precision and robustness, particularly in specialized fields like finance, and (3) the inability to deploy effectively on lightweight devices. To address these challenges, we introduce a framework uniquely designed to process and analyze massive volumes of argument data efficiently and accurately. This framework employs a text-to-text Transformer generation model as its backbone, utilizing multiple prompt engineering methods to fine-tune the model. These methods include Causal Inference from ChatGPT, which addresses the interpretability problem, and Prefix Instruction Fine-tuning as well as in-domain further pre-training, which tackle the issues of low robustness and accuracy. Ultimately, the proposed framework generates conditional outputs for specific tasks using different decoders, enabling deployment on consumer-grade devices. After conducting extensive experiments, our method achieves high accuracy, robustness, and interpretability across various tasks, including the highest F1 scores in the NTCIR-17 FinArg-1 tasks.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"MDPI","subitem_publisher_language":"en"}]},"item_10001_rights_15":{"attribute_name":"権利情報","attribute_value_mlt":[{"subitem_rights":"This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).","subitem_rights_language":"en"}]},"item_10001_source_id_9":{"attribute_name":"収録物ID","attribute_value_mlt":[{"subitem_source_identifier":"20799292","subitem_source_identifier_type":"EISSN"}]},"item_10001_version_type_20":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_1715043197608":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access"}]},"item_1722929371688":{"attribute_name":"出版社版DOI","attribute_value_mlt":[{"subitem_relation_name":[{"subitem_relation_name_language":"ja","subitem_relation_name_text":"10.3390/electronics13091746"}],"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.3390/electronics13091746","subitem_relation_type_select":"DOI"}}]},"item_1723180141928":{"attribute_name":"EID","attribute_value_mlt":[{"subitem_identifier_type":"URI","subitem_identifier_uri":"408133"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Ding, Fei","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"康, 鑫","creatorNameLang":"ja"},{"creatorName":"コウ, シン","creatorNameLang":"ja-Kana"},{"creatorName":"Kang, Xin","creatorNameLang":"en"}],"familyNames":[{"familyName":"康","familyNameLang":"ja"},{"familyName":"コウ","familyNameLang":"ja-Kana"},{"familyName":"Kang","familyNameLang":"en"}],"givenNames":[{"givenName":"鑫","givenNameLang":"ja"},{"givenName":"シン","givenNameLang":"ja-Kana"},{"givenName":"Xin","givenNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"769","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"292960/profile-ja.html","nameIdentifierScheme":"徳島大学 教育研究者総覧","nameIdentifierURI":"http://pub2.db.tokushima-u.ac.jp/ERD/person/292960/profile-ja.html"}]},{"creatorNames":[{"creatorName":"Wang, Linhuang","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"呉, 雨濃","creatorNameLang":"ja"},{"creatorName":"ゴ, ウノウ","creatorNameLang":"ja-Kana"},{"creatorName":"Wu, Yunong","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"ナカガワ, サトシ","creatorNameLang":"ja"},{"creatorName":"ナカガワ, サトシ","creatorNameLang":"ja-Kana"},{"creatorName":"Nakagawa, Satoshi","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"任, 福継","creatorNameLang":"ja"},{"creatorName":"ニン, フジ","creatorNameLang":"ja-Kana"},{"creatorName":"Ren, Fuji","creatorNameLang":"en"}],"familyNames":[{"familyName":"任","familyNameLang":"ja"},{"familyName":"ニン","familyNameLang":"ja-Kana"},{"familyName":"Ren","familyNameLang":"en"}],"givenNames":[{"givenName":"福継","givenNameLang":"ja"},{"givenName":"フジ","givenNameLang":"ja-Kana"},{"givenName":"Fuji","givenNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"401","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"19966/profile-ja.html","nameIdentifierScheme":"徳島大学 教育研究者総覧","nameIdentifierURI":"http://pub2.db.tokushima-u.ac.jp/ERD/person/19966/profile-ja.html"},{"nameIdentifier":"20264947","nameIdentifierScheme":"e-Rad_Researcher","nameIdentifierURI":"https://nrid.nii.ac.jp/ja/search/?qm=20264947"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2024-12-06"}],"displaytype":"detail","filename":"electronics_13_9_1746.pdf","filesize":[{"value":"749 KB"}],"format":"application/pdf","licensetype":"license_0","mimetype":"application/pdf","url":{"objectType":"fulltext","url":"https://tokushima-u.repo.nii.ac.jp/record/2012336/files/electronics_13_9_1746.pdf"},"version_id":"2d8d1ae5-f510-4982-9adf-2390a628590f"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"generative learning","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"financial argument analysis","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"prompt engineering","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"causal inference","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Causal Inference and Prefix Prompt Engineering Based on Text Generation Models for Financial Argument Analysis","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Causal Inference and Prefix Prompt Engineering Based on Text Generation Models for Financial Argument Analysis","subitem_title_language":"en"}]},"item_type_id":"40001","owner":"7","path":["1713853295607"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2024-12-06"},"publish_date":"2024-12-06","publish_status":"0","recid":"2012336","relation_version_is_last":true,"title":["Causal Inference and Prefix Prompt Engineering Based on Text Generation Models for Financial Argument Analysis"],"weko_creator_id":"7","weko_shared_id":-1},"updated":"2025-02-14T07:04:19.742092+00:00"}