Natural-language processing for computer-supported instruction

Barbara Maria Di Eugenio
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引用次数: 8

Abstract

Introduction Many computer applications are concerned with interpreting or producing instructions and fostering education, for example, 1. Animated agents that execute instructions [Webber 1995], 2. Systems that automatically produce instructional text (see Excerpt A in Figure 1) [Paris 1995], 3. Intelligent tutoring systems (ITS) that help a student master a certain subject [Anderson 1995, Schulze 2000], and 4. Systems that facilitate student collaboration [Soller 2001]. Of these four examples, only example two is a true natural-language (NL) system: The animated agents in example one may be instructed via a menu; an ITS may provide feedback to the student via graphics. However, all the systems in examples one, three, and four potentially benefit from a natural-language interface. For instance, consider the learning gain, that is, how much a student learns in a certain setting (S). Generally, the learning gain is the difference between the student's score on the same test, before and after (S). It has been shown that the learning gain of students interacting with an ITS is halfway between the learning gain of students that were exposed to the material in the usual classroom setting (lowest) and students that interact with a human tutor (highest) [Anderson 1995]. The difference in learning gain between students interacting with an ITS and those interacting with a human tutor is attributed conversation between tutor and student Figure 1 (see page 24) presents two samples of NL instructions: The first is taken from an online help file, the second from a tutoring dialogue. These two examples illustrate some of the problems faced by systems that must support the interpretation and generation of instructions. Instructions in a technical manual, online help, or home repair manual such as those under (A) in teach how to perform a task mainly by describing the steps to be performed. They often include descriptions of what will happen as the result of a certain step (e.g., the window expands to show the Alarm options in step two) as a way to inform the user whether he or she is on the right track. The structure of the text closely reflects the structure of the task, as has long been known regarding task-oriented discourse [Grosz and Sidner 1986]. Tutorial dialogues such as (B) in Figure 1 present a completely different approach to instruction. (Note: This excerpt is taken from a dialogue in the BEESIM project corpus [Rosé, Di Eugenio, and Moore …
计算机支持教学的自然语言处理
许多计算机应用与解释或生成指令以及促进教育有关,例如:执行指令的动画代理[Webber 1995], 2。自动生成教学文本的系统(参见图1中的摘录A) [Paris 1995], 3。智能辅导系统(ITS),帮助学生掌握某一学科[Anderson 1995, Schulze 2000],等。促进学生合作的系统[Soller 2001]。在这四个例子中,只有例子二是一个真正的自然语言系统:例子一中的动画代理可以通过菜单来指示;智能交通系统可以通过图形向学生提供反馈。然而,示例1、3和4中的所有系统都可能受益于自然语言接口。例如,考虑学习获得,也就是说,有多少学生学习在某些环境中(S)。一般来说,学习获得的区别是学生的分数相同的测试,之前和之后(年代)。它已经表明,学生的学习获得与它是介于交互学习获得的学生接触到的材料在平时的课堂环境与人类交互的(最低)和学生导师(最高)安德森[1995]。与人工智能互动的学生与与人类导师互动的学生之间的学习收益差异归因于导师与学生之间的对话。图1(见第24页)提供了两个NL指令示例:第一个取自在线帮助文件,第二个来自辅导对话。这两个例子说明了必须支持解释和生成指令的系统所面临的一些问题。技术手册、在线帮助或家庭维修手册中的说明,例如(a)项中的说明,主要通过描述要执行的步骤来教授如何执行任务。它们通常包括对某一步骤的结果的描述(例如,窗口展开以显示第二步中的警报选项),以告知用户他或她是否在正确的轨道上。文本的结构紧密地反映了任务的结构,这一点在任务导向语篇中早已为人所知[Grosz and Sidner 1986]。图1中的(B)等教程对话框呈现了一种完全不同的教学方法。(注:本文节选自BEESIM项目语料库中的一段对话[ros, Di Eugenio, and Moore…]
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