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。这个项目有两个问题：(1)使用计算机破译古代文本，(2)训练不使用平行文本的自动语言翻译系统。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什么作用，因为数据有限，迄今为止主要是人类的直觉。自动语言翻译的数据更加丰富，研究在21世纪取得了长足的进步。然而，研究人员沉迷于对大型平行文本的训练，这对于需要自动翻译的语言和领域的多样性是有限的。该项目开发了无监督的方法，利用语言知识的替代来源来弥补并行数据的缺乏。对于古代语言，这些来源包括已知的语言作为解密目标，利用语系内部的紧密联系。在翻译中，大量未翻译的数据被用来诱导强的双语联系。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性，以及在给定固定数量的脚本的情况下衡量丢失的语言是否可破译。古代文字的计算分析提供了对古代文化更好的理解，而无监督的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语言翻译提供了将更多语言对和领域带给广大人口的机会。

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.