Robust Incremental Semantic Resources for Dialogue

用于对话的强大增量语义资源

• 批准号: EP/J010383/1
• 负责人: Matthew Purver
• 金额: $ 12.26万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ010383%2F1
• 关键词: Robust; Incremental; Semantic; Resources; Dialogue; Robust; Incremental; Semantic; Resources; Dialogue

When humans process language, they do so incrementally, understanding and producing sentences on a word-by-word basis. In conversation, we easily switch roles between speaker and hearer mid-sentence, taking turns speaking and listening to show attention, clarify information or add detail when needed, interactively contributing to a shared, emerging picture of what we mean. If we want human-computer dialogue systems to be natural, efficient and easy to use, they must behave as incrementally as humans do: understanding and reacting interactively on a word-by-word basis rather than insisting on fully-formed sentences. We would prefer a system which behaves as in (1) below to the more familiar but annoying (2), or even the more patient but less interactive (3):(1)Usr: I'd like er [pause] . . .Sys: Yes?Usr: a ticket to Paris from, hang on . . . Sys: Paris, France? Usr: right, from London please. Sys: OK, checking for Paris to London.(2)Usr: I'd like er [pause] . . .Sys: I'm sorry, I don't understand. Please state your destination.(3)Usr: I'd like er [pause] . . .Usr: a ticket to Paris from, hang on . . . Usr: from London please. Sys: OK. Do you mean Paris, France?Previous research has developed computational models of dialogue which can behave incrementally, allowing the kind of interaction shown in (1); but they currently rely on hand-written rules or statistical models to relate words to actions and concepts. These lack the ability to express the complex meanings that human language is so good at conveying, and are time-consuming to create for any new system, domain or task. Instead, they need incremental models which deal with semantics, updating some representation of meaning as each word is heard or spoken, and which can be automatically learned from data; but general methods for doing this are currently lacking. This project will bridge this gap, providing a linguistically-based, learnable framework for incremental semantic interpretation and generation, which can be used to improve and extend existing dialogue systems.The project will start from recent work in theoretical linguistics and dialogue modelling which has produced the incremental semantic processing framework Dynamic Syntax (Kempson et al., 2001). This shows promise in modelling complex incremental dialogue, but is currently under-developed from a practical point of view, needing time-consuming expert hand-crafting, and missing a link between action planning and language generation. This project will address these issues. First, we will develop methods for automatically learning Dynamic Syntax grammars from data, allowing other researchers to easily produce and use their own versions in their own systems. Second, we will develop its methods for generating language so that it can be integrated with the way dialogue systems plan their actions on the fly. These new capabilities will be implemented computationally and evaluated on real data. Together, they will then be used to build a demonstration dialogue system which can behave incrementally, and will be packaged into a publicly available toolkit for researchers to develop their own incremental, semantic dialogue systems.

当人类处理语言时，他们会逐步这样做，以逐字了解和制作句子。在对话中，我们可以轻松地在扬声器和听众中间句子之间切换角色，轮流说话并在需要时倾听，澄清信息或在需要时添加细节，并互动地促进了我们的含义共享，新兴的图片。如果我们希望人类计算机对话系统是自然，高效且易于使用的，那么它们必须像人类一样逐步行事：逐字理解和互动地做出互动，而不是坚持完全形成的句子。我们更喜欢一个像以下（1）中（1）中（2）的系统，甚至更耐心，甚至更耐心，但互动较少（3）:( 1）USR：我想[暂停]。 。 。 。 。系统：巴黎，法国？ USR：对，请从伦敦。系统：好的，请检查巴黎到伦敦。（2）USR：我想[暂停]。 。 .Sys：对不起，我不明白。请陈述您的目的地。（3）USR：我想[暂停]。 。 .usr：一张来自巴黎的票，等等。 。 。 USR：请从伦敦。系统：好的。您的意思是法国巴黎吗？先前的研究开发了对话的计算模型，该模型可以逐步行事，允许（1）中显示的互动类型；但是他们目前依靠手写的规则或统计模型将单词与动作和概念相关联。这些缺乏表达人类语言如此擅长传达的复杂含义的能力，并且为任何新系统，域或任务创造时间耗时。取而代之的是，他们需要涉及语义的增量模型，在听到或说出每个单词时更新含义的某些表示形式，并且可以自动从数据中学习；但是目前缺乏这样做的一般方法。该项目将弥合这一差距，为逐步的语义解释和生成提供一个基于语言的，可学习的框架，可用于改善和扩展现有的对话系统。该项目将从理论语言学和对话建模的最新工作开始，从而产生了增量的语义处理框架动态语法（Kempson等人（Kempson et al。，2001年）。这显示了建模复杂的增量对话的希望，但目前从实际的角度开始不发达，需要耗时的专家手工制作，并且缺少行动计划和语言生成之间的链接。该项目将解决这些问题。首先，我们将开发从数据自动学习动态语法语法的方法，从而使其他研究人员可以轻松地在自己的系统中生产和使用自己的版本。其次，我们将开发其生成语言的方法，以便可以将其与对话系统计划计划的方式集成。这些新功能将在计算上实现，并根据实际数据进行评估。然后，他们将共同用于建立一个可以逐步行事的演示对话系统，并将包装成一个公开可用的工具包，以供研究人员开发自己的增量语义对话系统。