End-to-end integrated Statistical processing for Context-aware dialogue systems

该项目的目标是一个新的处理范式的开发和优化的口语对话系统（SDS），是上下文感知，高效，最重要的是强大的不确定性，弥漫在自然语言。我们将开发易于处理和有效的技术，用于上下文感知SDS中不确定性的集成端到端处理，使用结合部分可观察马尔可夫决策过程（POMDPs）的学习算法。这就要求我们开发有效的方法来训练和测试这些系统。我们还将通过对真实用户的系统测试来确定，与基于规则和基于标准MDP的技术相比，端到端的不确定性统计处理是否改善了用户的SDS。目前没有SDS将对话处理视为端到端的综合统计系统，受上下文的约束，其中一个过程中的不确定性馈送到其他过程，其中一个对话状态中的不确定性馈送到下一个对话状态，整个系统都受到上下文反馈的约束。通过只将一个最佳分析传递给更高级别的过程来忽略低级别过程输出中的不确定性仍然是标准做法，其副作用是低级别过程不考虑重要的高级别约束。例如，对话的上下文特征，如用户目标或先前的言语行为，在语音识别或话语解释中没有被系统地利用。这是当前SDS的一个严重缺陷，因为不确定性弥漫并扩散到对话处理的每一个级别，从语音识别错误到解释歧义，再到不确定的对话状态和竞争策略。这些问题导致了目前的情况，SDS是不是鲁棒或有效enoughfor任何，但非常简单的任务。我们将建立和评估SDS使用统计处理端到端，并使用contextrepresentation约束对话中固有的不确定性。我们将建立在TALK项目中开发的认证知识和技术，以及最近的语料库（COMMUNICATOR，TALK，AMI）上。在爱丁堡HCRC使用和开发的SDS开发工具、组件和环境（例如DIPPER、HTK、Festival）也提供了许多认证对话系统（FLIGHTS、TALK、WITAS），形成了一个平台，可以使用该项目中开发的新方法进行扩展。这些系统可以用于测试、评估和进一步的数据收集。因此，该提案旨在提高对话系统的稳健性和效率，并允许使用数据驱动的方法开发和优化SDS。目前部署的SDS有很多用户感到沮丧，因此从改进的健壮性和效率中可以获得很多好处。数据驱动的优化还将降低行业的部署和开发成本。因此，这项研究的受益者将可能是所有未来的IT用户（包括文盲和IT文盲，也在发展中国家）。在短期和中期，商业应用包括：交互式SDS、对话和会议摘要、交互式娱乐、智能辅导系统、智能个人助理和对话支持的问答和搜索。随着语音识别、解析、上下文敏感统计对话管理、部分可观察状态学习理论、新的、大型的、注释丰富的对话语料库的可用性、我们现在能够将对话处理视为端到端的上下文感知统计系统。我们相信这个模型将导致一个突破性的鲁棒，高效，自然的人机SDS，并有可能从根本上改善国家的最先进的对话管理。

This project targets a new processing paradigm for the development andoptimization of spoken dialogue systems (SDS) that are context-aware,efficient, and most importantly robust to the uncertainty thatpervades natural language. We will develop tractable and effectivetechniques for the integrated end-to-end treatment of uncertainty incontext-aware SDS, using learning algorithms combinedwith Partially Observable Markov Decision Processes (POMDPs). Thisrequires us to develop effective methods for training and testing suchsystems. We will also determine, through system tests withreal users, whether the end-to-end statistical treatment ofuncertainty improves SDS for users, in comparison to rulebased and standard MDP-based techniques.No current SDS treats dialogue processing as an end-to-endintegrated statistical system, constrained by context, whereuncertainty in one process feeds into other processes, whereuncertainty in one dialogue state feeds into the nextdialogue state, and where this whole system is constrained viacontextual feedback. It is still standard practice to ignore theuncertainty in the output of a lower-level process by passing only asingle best analysis to higher-level processes, with the sideeffect that lower-level processes do not take into account importanthigh-level constraints. For example, contextual features ofdialogues such as user goals or previous speech acts are notsystematically exploited in speech recognition or utteranceinterpretation. This is a serious shortcoming for current SDS, given that uncertainty pervades and proliferates throughevery level of dialogue processing, from speech recognition errorsthrough interpretation ambiguities, to uncertain dialogue states andcompeting strategies. These problems lead to the currentsituation where SDS are not robust or efficient enoughfor any but very simple tasks.We will build and evaluate SDS which usestatistical processing end-to-end, and which use contextrepresentations to constrain the uncertainty inherent in dialogue. Wewill build on exisiting knowledge and techniques developed in the TALKproject, and well as recent corpora (COMMUNICATOR, TALK, AMI). TheSDS development tools, components, and environments usedand developed at Edinburgh's HCRC (e.g. DIPPER, HTK, Festival) alsoprovide a number of exisiting dialogue systems (FLIGHTS, TALK, WITAS), forming a platform to be extended usingthe new methods developed in the project. These systems can then beused for testing, evaluation, and further data collection.The proposal thus aims to improve dialogue system robustness andefficiency, and allow SDS to be developed and optimized usingdata-driven approaches. There is much user frustration with currently deployed SDS, so there is much to be gained from improved robustness andefficiency. Data-driven optimization will also lead to decreaseddeployment and development costs for industry. Thus the beneficiaries ofthis research will potentially be all futureusers of IT (including the illiterate andIT-illiterate, also in the developing world). In the short tomedium term, commercial applications include: interactive SDS, dialogue and meeting summarisation, interactiveentertainment, intelligent tutoring systems, intelligent personalassistants, and dialogue supported question-answering and search.With recent advances in speech recognition, parsing, context-sensitivestatistical dialogue management, the theory of learning with PartiallyObservable states, the availability of new,large, and richly annotated dialogue corpora, we are now in a position to treat dialogueprocessing as an end-to-end context-aware statistical system. Webelieve this model will lead to a breakthough in robust, efficient, and natural human-computer SDS, andhas the potential to radically improve the state-of-the-art indialogue management.