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语言学习的计算性

The Computational Nature of Language Learning
课程网址: http://videolectures.net/mitworld_niyogi_cnl/  
主讲教师: Partha Niyogi
开课单位: 芝加哥大学
开课时间: 2012-02-10
课程语种: 英语
中文简介:
Partha Niyogi表示,当语言学习研究融入进化理论的见解时,它将变得更加强大。自然选择和人口变异的原则不仅在探索儿童如何学习语言,而且在语言如何随时间改变时发挥作用。 Nyogi说,所有语言都是可以学习的,或多或少可以统一学习,“学习汉语不需要25年,学习孟加拉语需要两年时间。”但是在过去的30多年里,Nyogi说,这里有很多语言。关于最有用的学习理论模型的辩论,努力解释人类语言能力如何在不同的发展阶段利用语言输入。一种语言习得模型描绘了孩子,他们掌握了基本的语法“地图”,从数据推断(与成人的互动),并通过某种算法组装语言的组成部分。已经进行了分析,好像存在“产生数据的目标语法,以及试图获得该目标语法的算法。”但是,Niyogi说,“世界上并非如此。”一个孩子接触到了很多来自其人口的变异;父母和其他人都产生不同的语法,不同的数据集。 Niyogi认为,“进化轨迹”将收购发生在个人层面,以及语言的变异如何从一代传到下一代。但是,你必须学习它,而不是继承你父母的语法。随着时间的推移检查语言变异,就好像它是遗传变异,“你得到一个不同的数学结构......并且概率开始发挥重要作用。”小的差异“会产生非常微妙的后果,导致非线性动力学演化的分叉。”例如1000年前,英国人说的是一种今天我们无法辨认的语言。如何通过学习模仿前一代的方式来说“我们已经远远超过了这一点?” Niyogi解释说,在一个人群中,两种不同的语言可能会竞争(例如,德语和英语类型的语法)。虽然大多数人可能会说主导变体,但有些孩子可能会接触到两者的混合物。在语言使用方面存在“漂移”,“突然之间,稳定变得不稳定。”在下一代,更多的学习者会选择少数变体。可以使用新表达式确定连续几代学习者的概率,并跟踪语言的进化转换。 Nyogi总结道,“无处不在的语言事实是它们会随着时间的推移而变化”,“甚至频率的轻微影响也可以消除看起来稳定的东西。”
课程简介: Language learning research becomes more robust when it incorporates insights from evolutionary theory, Partha Niyogi demonstrates. The principles of natural selection and variation in a population come into play not only when exploring how children learn language but how languages alter over time. All languages are learnable and more or less uniformly learnable, says Nyogi: “It doesn’t take 25 years to learn Chinese and two years to learn Bengali.” But in the last 30-odd years, Nyogi says, there’s been a great deal of debate about the most useful models of learning theory, with efforts to explain how the human language faculty makes use of linguistic input at different developmental stages. One model of language acquisition depicts the child, armed with a basic grammar “map,” extrapolating from data (interactions with adults), and assembling the components of language by some algorithm. Analysis has been conducted as if there were “a target grammar, which produces data, and an algorithm which is trying to acquire this target grammar.” But, says Niyogi, “that’s not true in the world.” A child is exposed to lots of variation from within its population; parents and others all produce different grammars, different data sets. Niyogi believes that an “evolutionary trajectory” links how acquisition happens at an individual level, and how variation in language springs up from one generation to the next. But rather than inheriting the grammar of your parents, you have to learn it. Examining language variation over time as if it were genetic variation, “you get a different mathematical structure…and probabilities start playing an important role.” Small differences “can have very subtle consequences giving rise to bifurcation in nonlinear dynamics of evolution.” For instance, 1000 years ago, the English were speaking a language that’s unrecognizable to us today. How has it come to be that “we have moved so far from that point through learning which is mimicking the previous generation?” Niyogi explains that within a single population two varying languages may be in competition (say, a German and an English-type grammar). While a majority may speak the dominant variant, some children will likely be exposed to a mixture of the two. There’s a “drift” in language use, “and suddenly, what was stable becomes unstable.” In the next generation, even more learners pick up the minority variant. It’s possible to determine the probability of learners in successive generations using new expressions, and tracking the evolutionary transformation of language. The “ubiquitous fact of languages is that they change with time,” concludes Nyogi, and “even a slight effect of frequency can wipe out something that looks stable.”
关 键 词: 语言学习; 自然选择; 人口变异; 语言习得模型
课程来源: 视频讲座网
最后编审: 2019-06-11:cjy
阅读次数: 30