Doctoral Dissertation: Investigating the role of grammatical representation in language learnability

博士论文：研究语法表征在语言可学习性中的作用

• 批准号: 1420785
• 负责人: Edward Gibson
• 金额: $ 1.17万
• 依托单位: Massachusetts Institute of Technology
• 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: 2014-07-15 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1420785&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Investigating; role; grammatical

Technologies which process natural language have become ubiquitous in the last decade. Web search engines, for example, process billions of pages of text, in order to determine which of those pages best match a user's search query. Many interfaces for interacting with computers -- for example, Apple's Siri personal assistant -- take voice-issued commands from their users, and must process these commands in order to follow the users' instructions. Finally, machine translation technologies have become available for many of the world's most common languages, allowing users to automatically translate text that they find in foreign books or websites. These technologies mostly rely on simple models of language, known as n-gram models or context-free grammars, which were developed in the 1950's and 1960's, and refined in later decades. These simple models of language have many advantages, most notably that they can be used to process large amounts of data very quickly. Because of their simplicity, however, these models are not able to capture many aspects of meaning in natural language. This has resulted in limitations for the technologies discussed above; virtual personal assistants are only able to process very simple types of instructions, and machine translations is still far from being as accurate as human translation. In the current project, Leon Bergen and Dr. Edward Gibson will be investigating more sophisticated kinds of language models, with the goal of increasing the ability of computers to understand language.Under the direction of Dr. Gibson, Mr. Berger will be studying language models known as mildly context-sensitive grammars. These grammars are able to express certain types of linguistic knowledge that humans have, but which cannot be expressed using simpler types of grammatical formalisms. For example, native speakers of English know that a declarative sentence like "Mary kicked the ball" is closely related in meaning to the question "What did Mary kick?" Although this fact seems obvious, it is difficult (or impossible) to express using simple types of grammars. However, mildly context-sensitive grammars can be used to express this knowledge in a very natural way. Mr. Bergen and Dr. Gibson will be studying whether mildly context-sensitive grammars can be automatically learned from examples of grammatical sentences. To do this, they will be using techniques from machine learning, a branch of computer science and statistics that develops algorithms that can automatically learn from data. The researchers will integrate these learning algorithms with their grammatical formalism, and will test whether their method learns an accurate grammar. The accuracy of the grammar will be evaluated using a corpus -- a collection of sentences -- in which every sentence has been manually annotated with its correct grammatical structure. If accurate mildly context-sensitive grammars can be learned in this manner, then this provides a potential method for improving the natural language processing technologies which were discussed above. In particular, because this method does not require an expert to write down the complete grammar for a language, it has the potential to be deployed without tremendous engineering effort, and may be deployed easily in foreign languages.

在过去的十年中，处理自然语言的技术已变得无处不在。 Web搜索引擎，例如处理数十亿页的文本，以确定其中哪些页面最能匹配用户的搜索查询。许多用于与计算机交互的接口（例如，苹果的Siri个人助理）从用户那里获取语音发出的命令，并且必须处理这些命令才能遵循用户的说明。最后，机器翻译技术已用于世界上许多最常见的语言，使用户可以自动翻译他们在外国书籍或网站中找到的文本。这些技术主要依赖于在1950年代和1960年代开发的简单语言模型，即N-gram模型或无上下文的语法模型，并在后来的几十年中进行了完善。这些简单的语言模型具有许多优势，最值得注意的是它们可以非常快速地处理大量数据。但是，由于它们的简单性，这些模型无法在自然语言中捕获意义的许多方面。这导致了上述技术的局限性；虚拟的个人助理只能处理非常简单的说明类型，而机器翻译仍然远非像人类翻译一样准确。在当前的项目中，莱昂·卑尔根（Leon Bergen）和爱德华·吉布森（Edward Gibson）博士将研究更复杂的语言模型，目的是提高计算机理解语言的能力。在吉布森（Gibson）的方向上，伯格（Berger）先生将研究称为温和上下文敏感语法的语言模型。这些语法能够表达人类拥有的某些类型的语言知识，但不能使用更简单的语法形式主义来表达。例如，以英语为母语的人知道，像“玛丽踢球”这样的声明性句子与“玛丽踢了什么？”的问题密切相关。尽管这一事实似乎很明显，但很难使用简单类型的语法表达（或不可能）。但是，有轻度上下文敏感的语法可用于以非常自然的方式表达这些知识。卑尔根先生和吉布森博士将研究是否可以自动从语法句子的例子中学到温和的上下文敏感语法。为此，他们将使用机器学习中的技术，这是计算机科学和统计的一个分支，这些分支开发了可以自动从数据中学习的算法。研究人员将将这些学习算法与语法形式主义相结合，并将测试他们的方法是否学会了精确的语法。语法将使用语料库（句子集合）评估语法的准确性，其中每个句子都用其正确的语法结构手动注释。如果可以以这种方式学习准确的温和上下文敏感的语法，那么这提供了一种潜在的方法来改善上面讨论的自然语言处理技术。特别是，由于这种方法不需要专家写下一种语言的完整语法，因此它有可能在没有巨大的工程工作的情况下将其部署，并且可以轻松地以外语部署。