采样的可信性作为句子处理的算法理论
Jacob Louis Hoover1,2, Morgan Sonderegger1, Steven T Piantadosi3
1McGill University, Montréal, Canada.
Open mind : discoveries in cognitive science
|August 28, 2023
概括
处理令人惊的单词需要更长的时间. 这项研究表明,以采样为基础的算法预测了阅读时间的超线性增长和惊人的差异,与现代语言模型的经验发现保持一致.
科学领域:
- 计算语言学 计算语言学
- 认知科学 认知科学
- 自然语言处理自然语言处理.
背景情况:
- 惊喜理论假设文字处理时间与上下文的惊喜相关.
- 现有的增量解析算法不能直接预测这种关系.
- 之前的模型假设处理成本的线性缩放具有惊喜性.
研究的目的:
- 为了研究运行时间随着惊喜而扩展的算法.
- 测试基于采样算法的预测与经验阅读时间数据的测试.
- 为了将新的预测与现有的惊喜理论进行对比.
主要方法:
- 开发并分析了基于采样的简单算法.
- 经验研究了估计的惊喜和阅读时间之间的关系.
- 利用现代语言模型进行意外估计.
主要成果:
- 采样算法预测阅读时间的超线性增长与惊喜.
- 这些算法还预测了阅读时间变异的增加.
- 经验数据证实了超线性缩放和随着语言模型的改进而增加的差异.
结论:
- 基于采样的算法提供了更准确的阅读时间和惊喜模型.
- 这些发现挑战了传统的惊喜理论的线性假设.
- 现代语言模型支持基于采样的计算模型的预测.
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