SHF: Small: Transforming Natural Language to Programming Languages

SHF：小：将自然语言转换为编程语言

• 批准号: 1423237
• 负责人: Xiaojin Zhu
• 金额: $ 25万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1423237&HistoricalAwards=false
• 关键词: SHF; Small; Transforming; Natural; Language

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.

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，随着我们手中无处不在的计算设备的出现，口袋、电视、汽车和眼镜，更流畅、更自然、更少干扰的输入方法是可取的。 除了传统键盘固有的局限性外，一个更根本的成本是重复和长期使用键盘所造成的伤害。 2010年，仅腕管综合征（CTS）就影响了近500万美国工人。CTS和肘管综合征（CBTS）一起占工人每3美元支出的1美元？赔偿除了这些经济成本，这些综合症严重限制了患者接触计算技术的能力，使他们无法从事计算机编程等职业。本项目旨在开发计算机编程的口语界面。 当前的目标是为流行的Java编程语言创建一个口语听写系统，该系统消除了程序员听写难以用语言表达的语法元素（如括号、括号、标点符号和大小写）的需要。 相反，系统将采用随机模型来从用户的模糊语音流中以高保真度推断预期节目。 该项目为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的更一般的框架奠定了基础。该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。 虽然传统的编程语言被设计为确定性语法，但任何更自然的人类语言习惯用法的语音输入必然会涉及歧义。 这种模糊性只能通过在计算机程序上开发准确和预测的概率模型来解决。 在这个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，以便更准确地将可能性分配给语音输入的各种解释。 从长远来看，这项研究奠定了坚实的语音工具包的各种计算语言，如特定领域的语言为汽车和娱乐设备和数据库查询语言的发展。