practical statistical machine translation

目前，占主导地位的统计机器翻译(SMT)范例利用成对的单词序列，即短语。尽管基于短语的SMT取得了成功，但它也存在一些众所周知的缺点，其中包括：没有(或很少)句法或语义敏感度，泛化能力差，对大型并行数据的依赖性很强。这严重限制了SMT作为一项独立技术在实践中的部署。在这个NSERC研究计划中，我想通过调查三个方面来解决这些限制。在第一个主题中，我将研究基于依存关系n元语法构建翻译引擎的方法：中心词(称为调控器)及其在句子中的依存关系集，由依存关系解析器(DP)或类似的设备识别。与标准短语相比，DP-ns有几个优点，包括在词汇表达能力和泛化能力之间有更好的折衷。本研究提案的第二个主题旨在解决机器翻译社区的一个关键挑战：证明语义信息有利于SMT。虽然这可能看起来很直观，但我们还没有经验地证明这一点。这项建议所追求的第三条也是最后一条途径是减轻当前SMT技术对并行培训材料可用性的强烈依赖。将研究并行语料库丰富以及改进的并行数据挖掘。

Currently, the dominant Statistical Machine Translation (SMT) paradigm capitalizes on pairs of sequences of words, namely phrases. Despite its success, phrase-based SMT has several well-understood shortcomings among which: no (or little) syntactical or semantic sensitivity, bad generalization capabilities, and a strong dependency on large parallel data. This severely limits in practice the deployment of SMT as a standalone technology. In this NSERC Research Program, I want to tackle these limitations by investigating three avenues.In the first theme, I will investigate ways of building translation engines based on dependency n-grams: a head word (called governor) and the set of its dependents in a sentence, as identified by a dependency parser (DP) or a comparable device. DP-ns have several advantages over standard phrases including a better compromise between lexical expressiveness and generalization power.The second theme of this research proposal is intended to solve part of a key challenge in the MT community: demonstrating that semantic information benefits SMT. As intuitive as this might appear, we have yet to demonstrate this empirically.The third and last avenue pursued in this proposal is the one of alleviating the strong dependency current SMT technology has on the availability of parallel training material. Parallel corpus enrichment as well as improved parallel data mining will be investigated.