有更多使用者的语言往往更难 (机器) 学习
Alexander Koplenig1, Sascha Wolfer2
1Leibniz Institute for the German Language (IDS), Mannheim, Germany. koplenig@ids-mannheim.de.
Scientific reports
|October 29, 2023
概括
计算语言模型显示,与普遍认为的相反,拥有更多发言者的语言实际上更难学习. 这项研究为语言获取和语言多样性提供了新的见解.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 语言获取 语言获取
背景情况:
- 语言模型 (LMs) 为研究人类语言提供了一个计算框架.
- 之前的研究表明,拥有较多发言人群的语言更容易学习.
研究的目的:
- 用LM来测试这样的假设,即使用LMs更容易学习拥有更多发言者的语言.
- 实证地研究说话人口数量与语言学习困难之间的关系.
主要方法:
- 在1293种语言的语料库中训练了各种LM,从n-gram到深度神经网络.
- 采用定量方法和机器学习来估计学习困难.
- 考虑到语言相关性 (族系遗传) 和地理接近.
主要成果:
- 发现语言学习困难和说话人口规模之间存在强有力的关系.
- 与预期相反,结果表明,说话人数较多的语言往往更难学习.
- 这一发现挑战了语言研究中以前的假设.
结论:
- 语言学习困难与使用者人口规模相反相关.
- 语言模型为探索语言假设提供了一个有价值的工具.
- 这项研究需要重新评估影响语言学习能力的因素.
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