RI: Multi-Level Modeling of Language and Translation

以往的统计机器翻译方法采用的是基于短语的模型，将短语表示为具有全屈折变化的单词序列，并且缺乏语言细节。 这种方法无法推广，并且基本上依赖于记忆在训练数据中观察到的单词和短语的翻译。本项目旨在通过引入更复杂的模型来提高SMT的质量，这些模型使用多层次的信息来表示短语。 这可以包括基本的语言信息，如词性、词元和一致性信息（格，数，人称），以及更详细的语言细节，包括语义类，论元结构，共指，短语边界和从句法中心词传播的信息。通过用这些信息注释所有数据并适当地扩展模型，从培训中可以学到比以往方法更多的东西。 现在有可能学习翻译看不见的单词，如果其他形式的单词出现;现在有可能学习关于语言的剑顺序的一般事实;现在有可能使用语言上下文来生成语法输出。这种泛化有可能导致更高质量的翻译，特别是对于只有少量训练数据的语言。 因此，它代表了一个显着的进步，以前的方法SMT。多层次模型有可能对所有语言技术的广泛影响。 不同层次的表示的同时建模是描述语言的一种非常有用和自然的方式。 该项目正在开发一个通用框架，用于创建语言和翻译的多层次概率模型，并探索其在翻译之外的任务中的应用，包括生成，释义和自然语言技术的自动评估。

Previous approaches to statistical machine translation (SMT) haveemployed phrase-based models which represent phrases as sequences offully-inflected words, and are otherwise devoid of linguisticdetail. Such approaches are unable to generalize and essentiallyrely on memorizing the translations of words and phrases that areobserved in training data.This project aims to improve the quality of SMT through theintroduction of more sophisticated models which represent phrasesusing multiple levels of information. This can include basiclinguistic information such as part of speech, lemmas, and agreementinformation (case, number, person), as well as more sophisticatedlinguistic detail including semantic classes, argument structure,co-reference, phrase boundaries, and information propagated fromsyntactic heads.By annotating all data with this information and extending modelsappropriately, there is the potential to learn much more fromtraining than was possible under previous approaches. There is nowthe potential to learn translations of unseen words if other forms ofthe words occur; it is now possible to learn general facts about a language'sword order; it is now feasible to use linguistic context to generategrammatical output. Such generalization has the potential to resultin much higher quality translation, especially for languages thatonly have small amounts of training data. It therefore represents asignificant advance over previous approaches to SMT.Multi-level models have the potential for wide-ranging impact onall language technologies. Simultaneous modeling of differentlevels of representation is an extremely useful and natural way ofdescribing language. This project is developing a general frameworkfor the creation of multi-level probabilistic models of language andtranslation, and exploring its application to tasks beyondtranslation including generation, paraphrasing, and the automaticevaluation of natural language technologies.