在一个孩子的语言经验中找到结构
Wentao Wang1, Wai Keen Vong1, Najoung Kim1,2
1Center for Data Science, New York University.
Cognitive science
|June 26, 2023
概括
在一个孩子的语言输入上训练的神经网络学习语法和语义词类. 视觉数据提供了轻微的收益,表明核心语言知识可以从有限的发展经验中学习.
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:
- 神经网络擅长自然语言处理,但需要大量的训练数据.
- 儿童获得语言的投入要比典型的AI模型少得多.
- 分布式学习者从有限的自然数据中提取语言知识的潜力未被充分探索.
研究的目的:
- 为了研究神经网络可以从单个孩子的发育语言数据中获得什么语言知识.
- 为了比较语言单独模型与视觉和语言模型的学习能力.
- 确定视觉输入对人工模型语言学习的影响.
主要方法:
- 在单个孩子的自我中心视觉数据和文本成绩单的纵向数据集上训练仅语言和视觉和语言神经网络.
- 分析语法 (例如名词,动词) 和语义类别的新兴词汇集群.
- 评估模型对语言可接受性的敏感性对比 (例如,协议,论证结构).
主要成果:
- 神经网络形成了与语法和语义类别相对应的词汇集群,反映了人类语言的获取.
- 模型表现出对语法可接受性现象的敏感性,例如确定词-名词协议和参数结构.
- 整合视觉信息在词语预测中提供了渐进的改进,特别是在视觉基础类别中,而不会从根本上改变语言表示.
结论:
- 相当多的语言知识,包括语法和语义分类,可以从有限的自然语言样本中学习.
- 视觉输入在人工模型中为语言学习提供了一些好处,但对于获得核心语言结构而言并不必不可少.
- 这项研究强调了从早期发展经验中学习基本语言知识的可用性,为人工智能和人类语言学习提供了洞察力.
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