SHF: Small: Transforming Natural Language to Programming Languages

就在几年前，我们的大部分计算输入还局限于传统的计算机键盘。现在，随着我们手中、口袋、电视、汽车和眼镜中无处不在的计算设备的出现，人们需要更流畅、更自然、更少分散注意力的输入方法。除了传统键盘的固有局限性之外，更根本的代价是反复和长期使用键盘所造成的伤害。2010年，仅腕管综合症（CTS）就影响了近500万美国工人。CTS和肘管综合征（CBTS）合计占工人每3美元支出中的1美元。补偿。除了这些经济成本之外，这些综合症严重限制了患者使用计算机技术的能力，使他们无法从事计算机编程等职业。这个项目旨在为计算机编程开发口语接口。当前的目标是为流行的Java编程语言创建口语听写系统，这样程序员就不需要口述难以用语言表达的语法元素，如括号、括号、标点符号和单词大小写。相反，系统将采用随机模型从用户的模糊语音流中高保真地推断出预期的程序。这个项目为一个更通用的框架奠定了基础，这个框架将模糊的自然人类语言与驱动计算机功能的各种形式语言和系统联系起来。本项目的关键技术创新在于开发计算机程序的随机模型。近年来，这种模型在推断人类语言的结构、意义和翻译方面取得了很大的成功。虽然传统编程语言被设计为确定性语法，但在更自然的人类语言习语中，任何语音输入都必然涉及歧义。这种模糊性可能只能通过在计算机程序上开发准确和可预测的概率模型来解决。本项目中要探索的模型包括传统的n-gram语言模型以及句法语言模型，这些模型利用编程语言的语法，以便更准确地为语音输入的各种解释分配可能性。从长远来看，这项研究为开发用于各种计算语言的强大语音工具包奠定了基础，例如用于汽车和娱乐设备的特定领域语言以及数据库查询语言。

Just a few years ago, the majority of our computational input was confined to the traditional computer keyboard. Now, with the advent of ubiquitous computational devices in our hands, pockets, televisions, cars, and glasses, more fluid, natural, and less distracting methods of input are desirable. Besides the inherent limitations of the traditional keyboard, a more fundamental cost is the number of injuries sustained by repeated and long-term keyboard use. Carpal Tunnel Sydrome (CTS) alone affected nearly 5 million US workers in 2010. CTS and Cubital Tunnel Syndrome (CBTS) together account for $1 of every $3 spent for workers? compensation. Besides these economic costs, these syndromes severely limit the ability of sufferers to access computational technology, locking them out of professions such as computer programming.This project aims to develop spoken language interfaces for computer programming. The immediate goal is to create a spoken language dictation system for the popular Java programming language, which removes the need for the programmer to dictate difficult-to-verbalize syntactic elements such as parentheses, brackets, punctuation, and word casing. Instead, the system will employ stochastic models to infer the intended program with high fidelity from the ambiguous speech stream of the user. This project lays the groundwork for a more general framework for relating ambiguous natural human language to the various formal languages and systems that drive the functioning of the computer.The key technical innovations of this project lie in the development of stochastic models for computer programs. Such models have met with much success in recent years for inferring the structure, meaning, and translations of human languages. While traditional programming languages are designed as deterministic grammars, any speech input in a more natural human language idiom will perforce involve ambiguity. This ambiguity may only be resolved by developing accurate and predictive probability models over computer programs. The models to be explored in this project include traditional n-gram language models as well as syntactic language models that make use of the programming language's grammar in order to more accurately assign likelihood to various interpretations of the speech input. In the long term, this research lays the groundwork for the development of robust speech toolkits for a wide variety of computational languages, such as domain-specific languages for cars and entertainment devices and database query languages.