探索机器阅读理解中的不可解答性:方法,基准和公开挑战
Hadiseh Moradisani1, Fattane Zarrinkalam1, Zeinab Noorian2
1College of Engineering, University of Guelph, Guelph, ON Canada.
概括
本综述探讨了机器阅读理解 (MRC) 系统处理无法回答问题的方法. 它对方法进行了分类,并确定了开发更强大的MRC系统的差距.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 机器阅读理解 (MRC) 系统通常假设问题在提供的文本中有答案.
- 现实世界中的应用程序经常会在给定的背景下提出缺乏答案的问题.
- 这种限制阻碍了现有的MRC技术的实际部署.
研究的目的:
- 综合审查现有的方法来解决MRC中无法回答的问题.
- 将这些方法分为模型不可知和模型特定的方法.
- 确定当前研究中的关键差距和挑战.
主要方法:
- 在MRC中处理无法回答问题的技术的系统文献审查.
- 基于方法的方法分类 (模型不可知与模型特定).
- 分析相关数据集和在现场使用的评估指标.
主要成果:
- 现有的方法被广泛分为模型不可知和模型特定的策略.
- 探讨了关键技术,数据集和评估指标.
- 发现的差距突出了未来研究和开发的领域.
结论:
- 开发能够可靠地识别和处理无法回答的问题的MRC系统至关重要.
- 需要进一步的研究来提高稳定性和通用性.
- 解决无法回答的问题将提高MRC系统的实际实用性.
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