CAREER: Semantic Divergences Across the Language Barrier

尽管在线内容在全球范围内呈爆炸式增长，但许多信息目前因语言障碍而被孤立。虽然多语言用户和翻译人员可以提供帮助，但在线内容的多样性和规模使得仅靠人类是不可能打破语言障碍的。需要自动化工具来支持和补充他们的工作。这个项目介绍了计算表示和方法，以比较和对比不同语言的文本的含义。通过为第二语言学习者、志愿翻译人员和安全分析师提供支持，所产生的模型将有助于开发能够支持跨语言交流和跨文化理解的语言技术，包括并增强机器翻译。这个职业项目将研究与教育相结合，通过使用翻译维基百科这一实际问题所激发的活动来说明基于不可避免的有偏见的数据开发的语言技术的挑战。这些活动面向计算机科学以外的高中生和本科生，以及本科和研究生水平的不同背景的计算机科学家。目前，自然语言处理中的跨语言工作依赖于这样的假设，即源文本及其翻译在两种语言中的意义相等，并且可以通过对齐句子、短语和单词将其分解为更小的对等单元。然而，用两种语言传达的内容很少是完全相同的：相同的主题或事件可以从非常不同的角度进行讨论，如果没有适当的语言和文化背景知识，即使是忠实的翻译也很难理解。该项目以机器翻译和语义分析方面的不同工作为基础，并将其联系起来，提供了检测和解释不同语言中单词和句子之间细微差异的技术。我们使用一组富有表现力的词和句子之间的语义关系来表征语义差异，即跨语言的意义差异。我们使用得到的模型来提高机器翻译的质量，并向不同背景的读者解释翻译。

Despite the explosion of online content worldwide, much information is currently isolated by language barriers. While multilingual users and translators can help, the diversity and scale of online content make it impossible for humans alone to break the language barrier. Automated tools are needed to support and supplement their work. This project introduces computational representations and methods to compare and contrast the meaning of text in different languages. The resulting models will be useful to develop language technology that can support cross-lingual communication, and cross-cultural understanding, including and augmenting machine translation, by providing support for second language learners, volunteer translators, and security analysts. This CAREER project integrates research with education by using activities motivated by the practical problem of translating Wikipedia to illustrate the challenges of language technology developed on inevitably biased data. These activities target high-school and undergraduate students outside of computer science, as well as computer scientists of diverse backgrounds at the undergraduate and graduate level.Cross-lingual work in natural language processing currently relies on the assumption that a source text and its translation are equivalent in meaning in the two languages, and that they can be decomposed into smaller equivalent units by aligning sentences, phrases and words. Yet, content conveyed in two languages is rarely exactly equivalent: the same topics or events can be discussed from widely different perspectives, and even faithful translations can be hard to understand without the appropriate linguistic and cultural background knowledge. Building on and connecting distinct bodies of work on machine translation and semantic analysis, this project provides techniques to detect and explain nuanced differences between words and sentences in different languages. We characterize semantic divergences, differences in meaning across languages, using an expressive set of semantic relations between words and sentences. We use the resulting models to improve machine translation quality, and to explain translations to readers of various backgrounds.