在MRC中基于解决文本模两可和语义模糊的问题,使用生成预训练的变压器进行问答
Muzamil Ahmed1, Hikmat Khan1, Tassawar Iqbal1
1Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan.
PeerJ. Computer science
|August 7, 2023
概括
本研究介绍了ExtGPT-QA,这是一种用于机器阅读理解 (MRC) 的新型模型,有效地解决了长段中文本模糊性和语义模糊性,提高了问题答案准确性.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 机器阅读理解 (MRC) 是一个具有挑战性的NLP任务,需要机器理解文本并回答问题.
- 目前的MRC模型与文本模糊性和语义模糊性扎,特别是在长段中.
- 深度学习和大型数据集已经改善了MRC,但局限性仍然存在.
研究的目的:
- 提出一个新的基于扩展生成预训练变压器的问答 (ExtGPT-QA) 模型.
- 在抽象的MRC系统中解决文本模糊性和语义模糊性.
- 为MRC任务生成准确和相关的答案.
主要方法:
- 开发了基于生成预训练变压器 (GPT) 的修改编码器和解码器架构的ExtGPT-QA模型.
- 在编码器中内置一个位置编码器来解决文本模两可的问题.
- 在解码器中使用了多头注意力机制,亲属和聚合层,以减轻语义模糊性.
- 应用语法和语义特征工程技术.
主要成果:
- ExtGPT-QA模型在基准数据集 (SQuAD,Wiki-QA,News-QA) 上表现出卓越的性能.
- 获得了93.25%的F1得分和90.52%的准确匹配得分.
- 性能优于现有的最先进的MRC技术.
结论:
- 拟议的ExtGPT-QA模型有效地处理了MRC中的文本模糊性和语义模糊性.
- 新的架构和功能工程提高了生成答案的精度和相关性.
- ExtGPT-QA代表了抽象机器阅读理解的重大进步.
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