Spreading the Word: The Theory of Distributed Representations in Speech and Natural Language Processing

传播信息：语音和自然语言处理中的分布式表示理论

• 批准号: RGPIN-2015-04069
• 负责人: Penn, Gerald
• 金额: $ 3.13万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=661912
• 关键词: Spreading; Word; Theory; Distributed; Representations

Motivated by recent progress in the use of neural networks in areas such as speech recognition and machine translation, this grant will fund research into the application of neural networks to a wide variety of other tasks in language technology.****Although much of the popular excitement over neural networks arises from their alleged correspondence to human cognitive mechanisms, what excites engineers and computer scientists about them is their utility as very compact sequence classifiers - tools that can accurately label long sequences of data, such as speech sounds or words of text, given only examples to train on.****Undertaken by one of the research labs that pioneered the use of neural networks in the area of speech recognition, the projects funded by this grant will consider not merely the transcription of speech into text, but the understanding of what was said or written. This emphasis on meaning will enable a new generation of language technology applications that can, for example, summarize large newspaper articles into their most relevant 10 or 20 percent. A key challenge to doing this is determining how individual representations of words combine to form representations of phrases and whole sentences. There is a branch of applied mathematics that studies this sort of combination, called mathematical linguistics, which will play a crucial role in this research. We will study the use of neural networks in mimicking those combinations.

由于神经网络在语音识别和机器翻译等领域的应用取得了最新进展，该基金将资助神经网络在语言技术中广泛应用的研究。虽然神经网络的大部分流行兴奋来自于它们与人类认知机制的对应，但令工程师和计算机科学家兴奋的是它们作为非常紧凑的序列分类器的实用性-可以准确标记长序列数据的工具，例如语音或文本单词，只给出训练的例子。该项目由一个率先在语音识别领域使用神经网络的研究实验室承担，该项目不仅考虑将语音转录成文本，而且考虑对所说或所写内容的理解。 这种对意义的强调将使新一代的语言技术应用成为可能，例如，可以将大型报纸文章总结为最相关的10%或20%。 这样做的一个关键挑战是确定单词的单个表示如何组合联合收割机以形成短语和整个句子的表示。 应用数学中有一个分支专门研究这类组合，叫做数学语言学，它将在这项研究中发挥至关重要的作用。 我们将研究神经网络在模拟这些组合中的应用。