Robust Incremental Semantic Resources for Dialogue

用于对话的强大增量语义资源

基本信息

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

项目摘要

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，他们会逐步进行，逐字理解并生成句子。在对话中，我们可以轻松地在句子中间切换说话者和听话者的角色，轮流说和听，以显示注意力，澄清信息或在需要时添加细节，以交互方式形成共享的、新兴的我们的意思图景。如果我们希望人机对话系统自然、高效且易于使用，它们的行为就必须像人类一样渐进：逐字理解并交互反应，而不是坚持完整的句子。我们更喜欢一个行为如下面 (1) 的系统，而不是更熟悉但烦人的 (2)，甚至更耐心但交互性较差的 (3):(1)Usr：我想要呃 [暂停] 。。 .Sys：是吗？Usr：一张从到巴黎的机票，请稍等。。。 Sys：法国巴黎？用户：好的，请从伦敦来。 Sys：好的，正在检查从巴黎到伦敦的航班。(2)Usr：我想要呃[暂停]。。 .Sys：对不起，我不明白。请说明您的目的地。(3)用户：我想要呃[暂停]。。 .Usr：一张从到巴黎的机票，请稍候。。。用户：请从伦敦来。系统：好的。您是指法国巴黎吗？之前的研究已经开发了对话的计算模型，它可以增量地表现，允许（1）中所示的交互；但他们目前依赖手写规则或统计模型将单词与动作和概念联系起来。它们缺乏表达人类语言擅长传达的复杂含义的能力，并且为任何新系统、领域或任务创建它们都非常耗时。相反，他们需要处理语义的增量模型，在听到或说出每个单词时更新某种意义的表示，并且可以从数据中自动学习；但目前缺乏这样做的通用方法。该项目将弥合这一差距，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。该项目将从理论语言学和对话建模方面的最新工作开始，这些工作已产生增量语义处理框架 Dynamic Syntax (Kempson et al., 2001)。这显示了对复杂增量对话进行建模的前景，但从实际角度来看，目前还不够成熟，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。该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，使其他研究人员能够轻松地在自己的系统中生成和使用自己的版本。其次，我们将开发其生成语言的方法，以便它可以与对话系统即时计划其行动的方式集成。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，它们将一起用于构建一个可以增量运行的演示对话系统，并将被打包到一个公开可用的工具包中，供研究人员开发自己的增量语义对话系统。