动词生成的视觉启发式:通过日语实验测试一个深度学习模型
Franklin Chang1, Tomoko Tatsumi2,3, Yuna Hiranuma1
1Department of English Studies, Kobe City University of Foreign Studies.
Cognitive science
|July 31, 2023
概括
演讲者使用场景中的视觉线索来选择动词时代,如过去和渐进形式. 一个深度学习模型证实了视觉事件特征和日语发言者的动词形态之间的这种联系.
科学领域:
- 认知科学 认知科学
- 语言学的语言学.
- 计算机科学 计算机科学
背景情况:
- 动词时代和方面形态通常与事件特征 (如 telicity) 有关.
- 演讲者从视觉场景中识别这些事件特征的认知机制在很大程度上是未知的.
研究的目的:
- 调查说话者如何从视觉场景中识别事件特征,以选择动词形态.
- 从视觉输入开发和测试一个从视觉输入生成动词的计算模型.
主要方法:
- 日本发言人描述了计算机生成的动画,其视觉特征与视觉性相关.
- 创建了一个深度学习模型来模拟视觉输入的动词生成.
- 测试了关于视频持续时间,动词复杂性和终点输入的模型预测.
主要成果:
- 成人和儿童成功地使用动画中的目标信息来选择适当的过去和渐进式动词形式.
- 一个深度学习模型复制了类似人类的动词形式分布,并准确地使用了形态选择的视觉线索.
- 模型预测证实,过去时的产量随着终点输入而增加,视频持续时间与动词复杂性有关.
结论:
- 动词生成与视觉启发学密切相关,这些启发学有助于事件理解.
- 这项研究提供了对视觉感知和语法形态学之间的相互作用的见解.
- 这些发现支持基于视觉输入的语言生产建模的连接主义方法.
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