Babble: domain-general methods for learning natural spoken dialogue systems

据估计，到2020年，对未来会话语音技术的需求将达到30亿美元的市场价值（Grand View Research，2014）。我们提出的技术将为快速开发具有深度语言理解能力的下一代自然交互式对话界面提供重要的基础和动力，适用于医疗保健、人机交互、可穿戴设备、家庭自动化、教育、游戏和辅助技术等领域。未来的对话语音界面应允许用户使用日常自发语言与机器进行交互，以满足日常需求。一个具有相当基本功能的商业例子是苹果的Siri。然而，即使是今天有限的语音接口，开发新的应用程序也非常困难和耗时：它们的关键组件目前需要由专家为特定的应用领域量身定制，依赖于手写的规则或统计方法，这些方法依赖于大量昂贵的，特定领域的，人工注释的对话数据。这样产生的组件对任何新的应用领域都没有什么用处，导致昂贵且耗时的开发周期。造成这种现状的一个关键原因是，对于口语对话，用于自然语言理解（NLU），对话管理（DM）和语言生成（NLG）的通用可扩展方法尚未可用。目前用于语言处理的领域通用方法是基于对话的，因此在处理书面文本时表现得相当好，但在口语对话的情况下，它们很快就会遇到困难，因为普通对话是高度碎片化和增量的：它自然地一个词一个词地发生，而不是一句一句地发生。真实的对话是一点一点地发生的，使用半开始，建议添加，暂停，中断和纠正-不尊重句子的边界。正是这些特性使得语音界面能够让人感觉到正常、自然的对话，而这正是当前最先进的语音界面所不能做到的。我们首次提出了将这两个问题结合起来解决的方法：（1）将领域通用的、增量的和可扩展的方法结合到NLU、DM和NLG中;（2）开发机器学习算法以使用（1）从数据自动创建工作语音接口。我们提出了一种新的方法“BABBLE”，在这种方法中，语音系统可以被训练成自然地与人类进行交互，就像一个孩子用新的单词组合来实验以发现它们的有用性一样（尽管这样做是为了避免在这样做的时候惹恼真实的用户！）。 BABBLE将作为开发工具包和移动的语音应用程序部署，供公众使用和参与，并将生成大型对话数据集，供科学和工业使用。这种新方法不需要昂贵的数据注释或专家开发人员，从而轻松创建新的语音界面，推动最先进的交互更自然，因此更成功，更有益于用户。与此相关的关键领域已经取得了新的进展：增量语法，对话的正式语义模型和样本有效的机器学习方法。联合收割机和发展这些方法的机会只是最近才出现的，现在使口语对话技术的重大进展成为可能。

The demand for future conversational speech technologies is estimated to reach a market value of $3 billion by 2020 (Grand View Research, 2014). Our proposed technology will provide vital foundations and impetus for the rapid development of a next-generation of naturally interactive conversational interfaces with deep language understanding, in areas as diverse as healthcare, human-robot interaction, wearables, home automation, education, games, and assistive technologies.Future conversational speech interfaces should allow users to interact with machines using everyday spontaneous language to achieve everyday needs. A commercial example with quite basic capabilities is Apple's Siri. However, even today's limited speech interfaces are very difficult and time-consuming to develop for new applications: their key components currently need to be tailor-made by experts for specific application domains, relying either on hand-written rules or statistical methods that depend on large amounts of expensive, domain-specific, human-annotated dialogue data. The components thus produced are of little or no use for any new application domain, resulting in expensive and time-consuming development cycles.One key underlying reason for this status quo is that for spoken dialogue, general, scalable methods for natural language understanding (NLU), dialogue management (DM), and language generation (NLG) are not yet available. Current domain-general methods for language processing are sentence-based and so perform fairly well for processing written text, but they quickly run into difficulties in the case of spoken dialogue, because ordinary conversation is highly fragmentary and incremental: it naturally happens word-by-word, rather than sentence-by-sentence. Real conversation happens bit by bit, using half-starts, suggested add-ons, pauses, interruptions, and corrections -- without respecting the boundaries of sentences. And it is precisely these properties that contribute to the feeling of being engaged in a normal, natural conversation, which current state-of-the-art speech interfaces fail to produce.We propose to solve these two problems together, by for the first time: (1) combining domain-general, incremental, and scalable approaches to NLU, DM, and NLG;(2) developing machine learning algorithms to automatically create working speech interfaces from data, using (1). We propose a new method "BABBLE" in which speech systems can be trained to interact naturally with humans, much like a child who experiments with new combinations of words to discover their usefulness (though doing this offline to avoid annoying real users while doing so!). BABBLE will be deployed as a developer kit and as mobile speech Apps for public use and engagement, and will also generate large dialogue data sets for scientific and industry use.This new method will not require expensive data annotation or expert developers, leading to easy creation of new speech interfaces that advance the state-of-the-art in interacting more naturally, and therefore more successfully and engagingly with users. New advances have been made in key areas relevant to this proposal: incremental grammars, formal semantic models of dialogue, and sample-efficient machine learning methods. The opportunity to combine and develop these approaches has arisen only recently, and now makes major advances in spoken dialogue technology possible.