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Why machines should analyze intention in natural language dialogue?

机译：为什么机器应该在自然语言对话中分析意图？

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摘要

One of the most difficult problems in Artificial Intelligence (AI) is to construct a natural language processing sysmtem which can interact with users through a natural language dialogue. The problem is difficult because there are so many ways by which a user can phrase his/her utterances to such a system. An added problem is that different types of users have different types of intentions and will conduct different exchanges with the system. While many have proposed theories and models of the processing of intentions in dialogue, few of these have been incorporated within working systems and tested empirically. Here, an experiment is conducted to test what we call the Intention-Computer Hypothesis: that the analysis of intention in natural-language dialogue facilitates effective natural-language dialogue between different types of people and a computer. The experiment provides evidence to support the hypothesis. In turn, the hypothesis provides evidence for a theory of intention analysis for natural-language dialogue processing. A central principle of the theory is that coherence of natural-language dialogue can be modelled by analyzing sequences of intention. A computational model, called Operating System CONsultant (OSCON), implemented in Quintus Prolog, makes use of the theory and hypothesis to understand, and answer in English, English questions about computer operating system.
机译：人工智能（AI）中最困难的问题之一是构建一种自然语言处理系统，该系统可以通过自然语言对话与用户进行交互。该问题是困难的，因为用户可以通过多种方式来表达他/她对这种系统的话语。另一个问题是，不同类型的用户具有不同类型的意图，并且将与系统进行不同的交换。尽管许多人提出了对话中意向处理的理论和模型，但其中很少有人被纳入工作系统并进行了实证检验。在这里，进行了一个实验，以测试我们所谓的意图计算机假说：自然语言对话中的意图分析有助于不同类型的人与计算机之间进行有效的自然语言对话。实验提供了支持该假设的证据。反过来，该假设为自然语言对话处理的意图分析理论提供了证据。该理论的中心原则是，可以通过分析意图序列来建模自然语言对话的连贯性。 Quintus Prolog中实现了一个称为操作系统顾问（OSCON）的计算模型，该模型利用理论和假设来理解和回答有关计算机操作系统的英语英语问题。

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