Robust Incremental Semantic Resources for Dialogue

当人类处理语言时，他们是在逐字理解和创造句子的基础上逐步进行的。在交谈中，我们很容易在句子中途在说话者和听话者之间切换角色，轮流说和听，以显示注意力，澄清信息或在需要时添加细节，交互地为我们的意思提供一个共享的、正在形成的图景。如果我们希望人机对话系统自然、高效、易于使用，它们必须像人类一样循序渐进：逐字理解和互动，而不是坚持使用完整的句子。我们更喜欢下面(1)中行为的系统，而不是更熟悉但更烦人的(2)，甚至更有耐心但交互更少的系统(3)：(1)USR：我想要呃[暂停]。。Sys：什么事？USR：一张去巴黎的票，请稍等。。。赛斯：巴黎，法国？USR：好的，请从伦敦出发。赛斯：好的，帮我查一下巴黎到伦敦的航班。(2)USR：我要呃[停顿]。。赛斯：对不起，我不明白。请说出你的目的地。(3)用户：我想呃[停顿]。。Usr：一张去巴黎的票，等一下。。。USR：请从伦敦出发。赛斯：好的。你是指法国巴黎吗？以前的研究已经开发出对话的计算模型，这种模型可以逐渐表现，允许(1)中所示的那种互动；但目前它们依赖手写规则或统计模型将词语与行动和概念联系起来。它们缺乏表达人类语言如此擅长传达的复杂含义的能力，并且为任何新的系统、领域或任务创造出来都是耗时的。相反，他们需要增量模型来处理语义，在听到或说出每个单词时更新一些含义表示，并且可以从数据中自动学习；但目前缺乏做到这一点的通用方法。该项目将弥补这一差距，为增量语义解释和生成提供一个基于语言学的、可学习的框架，可用于改进和扩展现有的对话系统。该项目将从最近在理论语言学和对话建模方面的工作开始，这些工作已经产生了增量语义处理框架动态句法(Kempson等人，2001年)。这在对复杂的增量对话进行建模方面显示出了希望，但从实用的角度来看，目前开发不足，需要耗时的专家手工制作，并且缺少行动规划和语言生成之间的联系。这个项目将解决这些问题。首先，我们将开发从数据中自动学习动态语法的方法，允许其他研究人员在他们自己的系统中轻松地生成和使用他们自己的版本。其次，我们将开发它的语言生成方法，以便它可以与对话系统计划其行动的方式相结合。这些新功能将通过计算实现，并根据真实数据进行评估。然后，它们将一起被用来建立一个可以递增行为的示范对话系统，并将被打包成一个公开可用的工具包，供研究人员开发自己的递增、语义对话系统。

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.