| Item type |
文献 / Documents(1) |
| 公開日 |
2024-12-06 |
| アクセス権 |
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|
アクセス権 |
open access |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版社版DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.3390/electronics13091746 |
|
|
言語 |
ja |
|
|
関連名称 |
10.3390/electronics13091746 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
|
|
タイトル |
Causal Inference and Prefix Prompt Engineering Based on Text Generation Models for Financial Argument Analysis |
|
言語 |
en |
| 著者 |
Ding, Fei
康, 鑫
Wang, Linhuang
呉, 雨濃
ナカガワ, サトシ
任, 福継
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
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. |
|
言語 |
en |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
generative learning |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
financial argument analysis |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
prompt engineering |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
causal inference |
| 書誌情報 |
en : Electronics
巻 13,
号 9,
p. 1746,
発行日 2024-05-01
|
| 収録物ID |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
20799292 |
| 出版者 |
|
|
出版者 |
MDPI |
|
言語 |
en |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
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/). |
| EID |
|
|
識別子 |
408133 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
electronics_13_9_1746.pdf (749 KB)
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