Developing a psychologically realistic generalisation mechanism within MOSAIC

儿童如何学习他们语言的语法范畴？例如，孩子们是如何学会“Dog”是名词，“Chase”是动词，以及如何在诸如“The WUG is Meeking the Blik”、“WUG”和“Blik”是名词、“Meeking”是动词的新奇句子中找到答案的？最近对语言学习的计算机模型的研究表明，其中一种方法是根据单词之前和之后的单词将单词分组。例如，在英语中，“a”和“the”之后以及“is”和“can”之前的单词往往是名词，而“is”和“can”之后以及“a”和“the”之前的单词往往是动词。然而，目前，以这种方式将单词组合在一起的计算机模型往往不能解释人类的语言学习，因为它们不像孩子一样逐渐学习，因此无法模拟儿童在不同发展阶段的行为。本研究的目的是开发一种更像儿童一样的类别学习模式。这将通过从最近的计算机模型中吸取想法来实现，这些模型在单个时间点将单词组合在一起，并将它们构建成一个称为马赛克的模型，该模型更渐进地学习语言。马赛克是一种计算机程序，它将针对语言学习儿童的语音作为输入，并产生像儿童一样的话语作为输出，随着模型处理更多的输入，这些话语会变得更长。因此，可以将这些话语与处于不同发展阶段的儿童的话语进行比较。在当前的项目中，我们将使用马赛克来开发一种更现实的方法，将单词按四种方式分组。首先，我们将测量不同类别(例如，名词和动词)在儿童语音记录中的生产率差异。这将为我们描述儿童在不同发展阶段对特定类别了解程度的差异，一个现实的语言学习模式应该能够模仿这种差异。其次，我们将构建该模型的几个不同版本，以不同的方式将单词分组到类别中。由于这些模型版本之间的唯一区别将是它们将单词分组到类别的方式，这将使我们能够看到哪种分组方法最有效。第三，我们将通过比较不同版本的模型的性能来评估它们。这将通过两种方式完成。首先，我们将看看这些模型在将单词归入成年人使用的类别方面做得有多好。其次，我们将看看这些模型在模仿儿童语言中特定发展阶段的概括模式方面有多好。最后，我们将看看是否有可能通过考虑人类(特别是年幼的儿童)在任何时候都能够处理多少信息来获得更好的结果。马赛克的一个有趣特征是，它学习如此缓慢的原因之一是，像人类一样，当它从以前没有听过的话语中学习时，它只能处理话语开始和结束时的信息。因此，马赛克可以用来观察限制用于将单词组合在一起的信息量是否会使模型的输出看起来更真实。

How do children learn the grammatical categories of their language? For example, how do children learn that "dog" is a Noun and "chase" is a Verb, and work out that in a novel utterance such as "The wug is meeking the blik", "wug" and "blik" are Nouns and "meeking" is a Verb? Recent research with computer models of language learning has shown that one way of doing this is to group words together on the basis of the words that come before and after them. For example, in English, words that come after "a" and "the" and before "is" and "can" tend to be Nouns, whereas words that come after "is" and "can" and before "a" and "the" tend to be Verbs. However, at the moment, computer models that group words together in this way tend to be unrealistic as explanations of human language learning because they do not learn gradually like children, and so cannot mimic the behaviour of children at different points in development. The aim of this research is to develop a more child-like model of category learning. This will be done by taking ideas from recent computer models that group together words at a single point in time, and building them into a model called MOSAIC that learns language more gradually. MOSAIC is a computer program that takes as input speech directed at language-learning children, and produces as output child-like utterances that get longer as the model processes more input. These utterances can therefore be compared with those of children at different points in development. In the current project, we will use MOSAIC to develop a more realistic way of grouping words into categories in four ways. First, we will measure differences in the productivity of different categories (e.g. Noun and Verb) in transcripts of children's speech. This will give us a description of differences in how much children know about particular categories at different points in development, which a realistic model of language learning should be able to mimic. Second, we will build several different versions of the model that group words into categories in different ways. Since the only difference between these versions of the model will be the way in which they group words into categories, this will allow us to see which grouping method is most effective. Third, we will evaluate the different versions of the model by comparing their performance with respect to each other. This will be done in two ways. First we will look at how good the models are at grouping words into the kind of categories used by adults. Second, we will look at how good the models are at mimicking the pattern of generalisation in children's speech at particular points in development. Finally, we will look at whether it is possible to get better results by taking account of how much information humans (and particularly young children) are able to deal with at any one time. An interesting feature of MOSAIC is that one of the reasons why it learns so slowly is that, like humans, when learning from an utterance it hasn't heard before, it can only deal with information at the beginning and the end of the utterance. MOSAIC can therefore be used to look at whether limiting the amount of information that is used to group words together makes the output of the models look more realistic.