practical statistical machine translation

目前，占主导地位的统计机器翻译（SMT）范式利用了词对序列，即短语。尽管取得了成功，但基于短语的SMT有几个众所周知的缺点，其中包括：没有（或很少）语法或语义敏感性，泛化能力差，以及对大型并行数据的强烈依赖。这在实践中严重限制了SMT作为独立技术的部署。在这个国家科学研究委员会的研究项目中，我想通过研究三个途径来解决这些限制。在第一个主题中，我将研究基于依赖项n-grams构建翻译引擎的方法：由依赖项解析器（DP）或类似设备识别的句子中的头词（称为调控器）及其依赖项集。与标准短语相比，DP-ns有几个优点，包括在词汇表达性和泛化能力之间更好地折衷。本研究计划的第二个主题旨在解决MT社区的部分关键挑战：证明语义信息有利于SMT。尽管这看起来很直观，但我们还没有从经验上证明这一点。本建议所追求的第三个也是最后一个途径是减轻当前SMT技术对平行培训材料的供应的强烈依赖。将研究并行语料库富集和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.