从接地语言中学习函数词的含义 使用视觉问题答案模型
Eva Portelance1,2, Michael C Frank3, Dan Jurafsky4
1Department of Linguistics, McGill University.
Cognitive science
|May 14, 2024
概括
儿童可能通过统计学学习学习复杂的函数单词,而不是天生的知识. 在视觉数据上训练的神经网络证明了这一点,学习"或"和"和"等词的空间和逻辑推理.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 发展心理学 发展心理学
背景情况:
- 像"或"和"后面"这样的函数词需要复杂的推理.
- 以前的理论往往假定先天的语言知识来学习这些单词.
- 神经网络的近期进展表明,从上下文中学习单词含义的潜力.
研究的目的:
- 调查儿童和人工模型如何学习函数词.
- 了解视觉接地在学习单词含义中的作用.
- 探索统计学学习是否可以解释函数词汇获取.
主要方法:
- 利用循环神经网络,对视觉接地语言任务进行训练.
- 分析了模型学习的函数词的语义表示.
- 测试模型的理解逻辑连接词 ("和"",或") 和空间关系的能力.
主要成果:
- 模型学习了函数词的梯度语义,捕捉空间和数值推理.
- 模型在没有事先的逻辑知识的情况下获得了"和"和"或"的含义.
- 单词学习困难与输入频率相关,表明统计学学习模式.
- 观察到早期对替代语言表达方式敏感的证据.
结论:
- 非象征性的统计学习算法可以在视觉接地语境中获得微妙的函数词语含义.
- 这为儿童的函数词学习提供了潜在的机制,挑战了先天的知识假设.
- 视觉接地学习为理解语言学习提供了一个有希望的途径.
关键词:
函数词的获取 函数词的获取逻辑推理逻辑推理的推理多模式的统计学习.神经网络模型的神经网络模型概念验证的概念证明推理技巧 推理技巧 推理技能视觉问题解答 视觉问题解答视觉接地语言是一种视觉接地语言.更多相关视频
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