Formalstyler: GPT-Based Model for Formal Style Transfer with Meaning Preservation

Author:

de Rivero Mariano,Tirado Cristhiam,Ugarte WillyORCID

Abstract

AbstractStyle transfer is a natural language processing generation task, it consists of substituting one given writing style for another one. In this work, we seek to perform informal-to-formal style transfers in the English language by using a style transfer model that takes advantage of the GPT-2. This process is shown in our web interface where the user input a informal message by text or voice. Our target audience are students and professionals in the need to improve the quality of their work by formalizing their texts. A style transfer is considered successful when the original semantic meaning of the message is preserved after the independent style has been replaced with a formal one with a high degree of grammatical correctness. This task is hindered by the scarcity of training and evaluation datasets alongside the lack of metrics. To accomplish this task, we opted to utilize OpenAI’s GPT-2 Transformer-based pre-trained model. To adapt the GPT-2 to our research, we fine-tuned the model with a parallel corpus containing informal text entries paired with the equivalent formal ones. We evaluate the fine-tuned model results with two specific metrics, formality and meaning preservation. To further fine-tune the model, we integrate a human-based feedback system where the user selects the best formal sentence out of the ones generated by the model. The resulting evaluations of our solution exhibit similar to improved scores in formality and meaning preservation to state-of-the-art approaches.

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. NoHateS: A Transformers-based Approach for Real-Time Hate Speech Detection in Spanish;2023 IEEE XXX International Conference on Electronics, Electrical Engineering and Computing (INTERCON);2023-11-02

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3