Generating under Global Constraints: The Case of Scripted Dialogue
Generating under Global Constraints: The Case of Scripted Dialogue
复制标题
全球约束下的生成：脚本对话的案例
DOI：
10.1007/s11168-008-9049-3
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Kees van Deemter
中科院分区：
文献类型：
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作者：
P. Piwek;Kees van Deemter
最近，自然语言生成（nlg）作为一个约束满足问题（csp）的观点已经看到了复苏的东西。本文的目的是研究当nlg被视为csp时出现的问题，并介绍一种基于约束的nlg的新应用，即脚本对话。脚本对话与语篇有许多共同的关键特征，这使得计算机生成对话的全局属性可以像生成语篇一样控制。我们特别注意使用软约束来执行文本和对话的全局属性。由于一直很少有研究到软约束的形式属性有关的生成，我们开始了理论探索。我们认为，当涉及到多个约束时，在提出具体的算法之前，正确地定义什么是优化的是很重要的，我们认为这样的定义在基于csp的nlg中经常缺乏。我们表明，它可以是困难的（有时甚至是不可能的），以保证满足全球的限制，按照当地的战略。基于这些困难，我们提出了一种新的方法来生成话语和对话，它结合了csp求解和修改。脚本对话是用来说明这种方法，这是比较替代方案，如监测和估计。
Recently, the view of Natural Language Generation (nlg) as a Constraint Satisfaction Problem (csp) has seen something of a revival. The aim of this paper is to examine the issues that arise when nlg is viewed as a csp, and to introduce a novel application of constraint-based nlg, namely the Scripted Dialogue. Scripted Dialogue shares a number of crucial features with discourse, which make it possible to control the global properties of a computer-generated dialogue in the same way as those of a generated discourse. We pay particular attention to the use of soft constraints for enforcing global properties of text and dialogue. Because there has been little research into the formal properties of soft constraints in relation to generation, we start out with a theoretical exploration. We argue that, when multiple constraints are involved, it is important to define properly what is being optimised before proposing specific algorithms, and we argue that such definitions are often lacking in csp-based nlg. We show that it can be difficult (and sometimes even impossible) to guarantee satisfaction of global constraints by following local strategies. Based on these difficulties, we propose a novel approach to the generation of discourse and dialogue which combines csp solving with revision. Scripted Dialogue is used to illustrate this approach, which is compared with alternatives such as monitoring and estimation.