RI: Medium: Deciphering Natural Language (DECIPHER)

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。该项目承担两个问题：（1）使用计算机破译古代文本，（2）在不使用平行文本的情况下训练自动语言翻译系统。 迄今为止，统计语言处理软件在古代文本分析中几乎没有发挥作用，因为数据有限，人类直觉迄今为止占主导地位。 自动语言翻译的数据更加丰富，研究在世纪取得了长足的进步。 然而，研究人员沉迷于大型并行文本的训练，这是有限的，因为人们需要自动翻译的语言和领域的多样性。该项目开发了无监督的方法，以弥补缺乏并行数据，使用替代的语言知识来源。 对于古代语言，这些来源包括已知的语言作为解密目标，利用语言家族内的紧密联系。 在翻译中，大量的未翻译的数据被用来诱导强有力的双语联系。 在解密框架中制定这些任务带来了强大的密码学理论和算法。 这种理论还有助于在给定固定数据资源的情况下估计预期的翻译准确度，并在给定固定数量的文字的情况下衡量一种丢失的语言是否可破译。对古代文字的计算分析提供了对古代文化的更好理解，而无监督技术构建了历史语言学家非常感兴趣的语言联系。 将这些技术应用于自动化语言翻译提供了将更多的语言对和领域带给大众的机会。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This project takes on two problems: (1) deciphering ancient texts using computers, and (2) training automated language translation systems without using parallel texts. Statistical language processing software has played little role to date in the analysis of ancient texts, where data is limited and human intuition has so far ruled. Data for automated language translation is more plentiful, and research has made great strides in the 21st century. However, researchers are addicted to training on large parallel texts, which are limited for the diversity of languages and domains for which people need automated translation.The project develops unsupervised methods that compensate for the lack of parallel data, using alternative sources of linguistic knowledge. For ancient languages, these sources include known languages as decipherment targets, capitalizing on tight connections within a language family. In translation, large quantities of untranslated data are exploited to induce strong bilingual connections. Formulating these tasks in a decipherment framework brings powerful cryptographic theory and algorithms to bear. Such theory also helps estimate expected translation accuracy given fixed data resources, and gauge whether a lost language is decipherable, given a fixed amount of script.Computational analysis of ancient scripts offers a better understanding of ancient cultures, and unsupervised techniques construct language connections of great interest to historical linguists. Applying such techniques to automated language translation offers the chance to bring many more language pairs and domains to the population at large.