The role of noise in information-theoretic models of sentence comprehension and production

噪声在句子理解和生成的信息论模型中的作用

• 批准号: 1534318
• 负责人: Edward Gibson
• 金额: $ 36.69万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1534318&HistoricalAwards=false
• 关键词: role; noise; information; theoretic; models

Human language as it is produced and understood is full of errors: people make speech errors, they make typographical errors when typing / texting, and there is often background noise that makes it impossible to perceive words accurately. Given the noisy nature of human language in practice, it is surprising that people can understand one another so well. The question of how people can communicate given noise is not yet solved, and is the focus of our work. Understanding how humans can understand noisy language is critical for two reasons. First, language technologies must be capable of processing noisy language input: translation services need to account for errors in the text being translated; search engines need to process noisily-generated web content. Evidence concerning how humans understand language in noise can lead to improvements in the design of language technologies. In addition, until dialogue systems can produce coherent language responses--likely decades away--any practical application of such systems must be designed with an understanding of how humans deal with noisy or confusing language input. Second, on the clinical side, understanding how humans understand language which might contain errors will provide insights into language comprehension disorders. Recent research has shown that individuals with aphasia appear to assume the presence of more errors in the input than healthy participants, and thus show stronger reliance on their prior beliefs about the world when interpreting language. Applications of this work may lead to more efficient diagnosis and treatment options for such patients.The goals of the proposed research are two-fold. First, the researchers will investigate noise in the process of language comprehension, where noise falls into three categories (a) deletions, such that the listener / reader might miss something that was intended; (b) insertions, such that the producer might accidentally insert something; and (c) swaps, such that the producer might accidentally switch elements in the stream. Second, the researchers will investigate an information-theoretic approach to memory in sentence production, where memory is a source of potential errors in language use. Recent human vision research suggests that memory capacity is best modeled as a limitation on the complexity of the representations, in terms of information-theoretic units called "bits". Simple representations require very few bits of information, but complex representations require many. The proposed research extends this idea to language, such that high-frequency words and phrases such as "the boy sees the girl" should be stored easily in memory, while less frequent components such as "the woman who the man met was tall" should be difficult to store in memory.

人类的语言被产生和理解的是充满错误：人们犯了语音错误，在打字 /发短信时会出现印刷错误，并且经常有背景噪音使得无法准确感知单词。 考虑到人类语言在实践中的嘈杂性，人们可以很好地理解彼此。人们如何交流给定噪声的问题尚未解决，这是我们工作的重点。了解人类如何理解嘈杂的语言至关重要，原因有两个。首先，语言技术必须能够处理嘈杂的语言输入：翻译服务需要说明要翻译文本中的错误；搜索引擎需要处理嘈杂的网络内容。关于人类如何理解噪声语言的证据可以改善语言技术的设计。此外，直到对话系统可以产生连贯的语言响应（几十年来）之前，任何对此类系统的实际应用都必须以了解人类如何处理嘈杂或令人困惑的语言输入。其次，从临床方面来说，了解人类如何理解可能包含错误的语言将提供对语言理解障碍的见解。最近的研究表明，失语症的个人似乎比健康参与者承担更多的意见错误，因此在解释语言时对他们对世界的先前看法更依赖。这项工作的应用可能会导致此类患者更有效的诊断和治疗选择。拟议的研究的目标是两倍。 首先，研究人员将在语言理解过程中调查噪声，在语言理解过程中，噪声分为三类（a）删除，以使听众 /读者可能会错过任何预期的东西。 （b）插入，使生产者可能会意外插入一些东西； （c）交换，使生产者可能会意外切换流中的元素。 其次，研究人员将研究一种信息理论方法在句子生产中的记忆方法，其中记忆是语言使用中潜在错误的根源。 最近的人类视力研究表明，在称为“位”的信息理论单元方面，最好将记忆能力建模为对表示的复杂性的限制。 简单的表示需要很少的信息，但是复杂的表示需要很多。拟议的研究将这一想法扩展到语言，因此高频单词和短语（例如“男孩看到女孩”）应该很容易存储在记忆中，而诸如“男人遇见的女人身高的女人”等频繁的组成部分应该很难存储在记忆中。