Scaling up Statistical Spoken Dialogue Systems for real user goals using automatic belief state compression

语音对话系统（SDS）越来越多地被部署在各种商业应用中，从传统的呼叫中心自动化（例如旅游信息）到新的“故障排除”或客户自助服务线路（例如帮助修复断开的互联网连接）。SDS是出了名的脆弱（尤其是对语音识别错误），不能提供自然的易用性，也不能适应不同的用户。SDS的主要问题之一是在不确定的情况下保持用户在对话中的目标的准确视图（例如，在附近找到一家好的印度餐馆，或修理宽带连接），从而计算最佳的下一个系统对话（例如，提供餐馆，要求澄清）。最近对统计口语对话系统（SSDS）的研究已经成功地解决了这些问题的各个方面，但是，我们将表明，它目前受到用户目标的贫乏表示的阻碍，这已被采用标准技术来实现易于处理的学习。在整个领域，目前只有小的和不现实的对话问题（通常少于100个可搜索的实体）是用统计学习方法解决的，因为计算的可追溯性。此外，当前ssd中的用户目标状态近似不可能表示一些合理的用户目标，例如，有人想知道附近的便宜餐馆和更远的高质量餐馆。这使得对话管理不太理想，并且无法充分处理以下类型的用户话语：“我正在寻找法国或意大利食物”和“除非它很贵，否则不要意大利菜”。带有各种否定和中断的用户话语是非常自然的，并且充分利用了自然语言输入的力量，但目前的ssd无法充分处理它们。此外，对话系统评估中的许多工作表明，真正的用户目标通常是具有不同功能的项目集合，而不是单个项目。人们喜欢探索物品功能之间可能的折衷。因此，我们的主要建议是：a)开发具有准确、扩展的用户目标表示的现实的大规模ssd，以及b)使用新的自动信念压缩（ABC）技术来规划由此生成的大型状态空间。像Value-Directed Compression这样的技术证明了可以在SSDS域中自动找到可压缩结构（例如，将433个状态的测试问题压缩到31个基本函数）。这些技术的根源在于处理现实环境中健壮的机器人导航所需的最大状态空间的方法，并可能导致在开发健壮、高效和自然的人机对话系统方面取得突破，并有可能从根本上提高对话管理的最新水平。

Spoken dialogue systems (SDS) are increasingly being deployed in avariety of commercial applications ranging from traditional CallCentre automation (e.g. travel information) to new ``troubleshooting''or customer self-service lines (e.g. help fixing broken internetconnections).SDS are notoriously fragile (especially to speech recognition errors),do not offer natural ease of use, and do not adapt to differentusers. One of the main problems for SDS is to maintain an accurateview of the user's goals in the conversation (e.g. find a good indianrestaurant nearby, or repair a broadband connection) underuncertainty, and thereby to compute the optimal next system dialogueaction (e.g. offer a restaurant, ask for clarification). Recentresearch in statistical spoken dialogue systems (SSDS) hassuccessfully addressed aspects of these problems but, we shall show,it is currently hamstrung by an impoverished representation of usergoals, which has been adopted to enable tractable learning withstandard techniques.In the field as a whole, currently only small and unrealistic dialogueproblems (usually less than 100 searchable entities) are tackled withstatistical learning methods, for reasons of computationaltractability.In addition, current user goal state approximations in SSDS make itimpossible to represent some plausible user goals, e.g. someone whowants to know about nearby cheap restaurants and high-quality onesfurther away. This renders dialogue management sub-optimal and makesit impossible to deal adequately with the following types of userutterance: ``I'm looking for french or italian food'' and ``NotItalian, unless it's expensive''. User utterances with negations anddisjunctions of various sorts are very natural, and exploit the fullpower of natural language input, but current SSDS are unable toprocess them adequately. Moreover, much work in dialogue systemevaluation shows that real user goals are generally sets of items withdifferent features, rather than a single item. People like to explorepossible trade offs between features of items.Our main proposal is therefore to: a) develop realistic large-scale SSDS with an accurate, extended representation of user goals, and b) to use new Automatic Belief Compression (ABC) techniques to plan over the large state spaces thus generated.Techniques such as Value-Directed Compression demonstrate thatcompressible structure can be found automatically in the SSDS domain(for example compressing a test problem of 433 states to 31 basisfunctions).These techniques have their roots in methods for handling the largestate spaces required for robust robot navigation in realenvironments, and may lead to breakthroughs in the development ofrobust, efficient, and natural human-computer dialogue systems, withthe potential to radically improve the state-of-the-art in dialoguemanagement.