Massively parallel computing in natural language technology

自然语言技术中的大规模并行计算

• 批准号: 389655-2010
• 负责人: Penn, Gerald
• 金额: $ 10.26万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments - Category 1 (<$150,000)
• 财政年份: 2009
• 资助国家: 加拿大
• 起止时间: 2009-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=442974
• 关键词: Massively; parallel; computing; natural; language

While the best known trend in speech and language processing over the last twenty years is probably still its adoption of statistical methods to crack open a variety of previously intractable computing applications (speech recognition, machine translation, web-page search, etc.), this field has recently turn another, very different, less celebrated, but equally transformative corner. State of the art research on many of these applications can now be spotted not so much by their radically new techniques, although innovations have of course still been forthcoming, as by their radically larger **scales**. A great many machine translation groups still essentially use IBM's models from the early 1990s, but trained over half a petabyte (a megabyte of megabytes) of data. The best text-to-speech synthesizers manipulate speech signals in about the same way as their predecessors did 15 years ago, in fact, but they paste together pieces of recorded speech from databases that are tens of thousands of times larger than they were back then. Bigger now does actually go a very long way towards producing better.

虽然在过去的二十年里，语音和语言处理领域最著名的趋势可能仍然是采用统计方法来破解各种以前难以处理的计算应用（语音识别，机器翻译，网页搜索等），这一领域最近出现了另一个非常不同的、不那么著名但同样具有变革性的转折点。 对许多这些应用的最新研究现在可以通过其全新的技术来发现，尽管创新当然仍然是即将到来的，因为它们的规模非常大。 许多机器翻译团队仍然基本上使用20世纪90年代早期的IBM模型，但训练了超过半PB（兆字节）的数据。 事实上，最好的文本到语音合成器处理语音信号的方式与15年前的前辈大致相同，但它们将来自数据库的记录语音片段粘贴在一起，这些数据库比当时大数万倍。 现在，更大的规模实际上对生产更好的产品大有帮助。