基于强化学习和机器翻译质量评估的中朝机器翻译研究

针对目前机器翻译模型存在的曝光偏差和译文多样性差的问题,提出一种基于强化学习和机器翻译质量评估的中朝神经机器翻译模型QR-Transformer.首先,在句子级别引入评价机制来指导模型预测不完全收敛于参考译文;其次,采用强化学习方法作为指导策略,实现模型在句子级别优化目标序列;最后,在训练过程中融入单语语料并进行多粒度数据预处理以缓解数据稀疏问题.实验表明,QR-Transformer有效提升了中朝神经机器翻译性能,与Transformer相比,中—朝语向BLEU值提升了5.39,QE分数降低了5.16,朝—中语向BLEU值提升了2.73,QE分数下降了2.82.     

一种基于实例的汉英机器翻译策略

介绍了一种基于实例的汉英机器翻译策略,重点讨论了汉英双语语料库的设计和基于该语料库的汉语句子的匹配算法。在进行汉语句子的匹配时,根据汉语的特点直接采用汉字的匹配,而没有进行汉语句子的分词。另外,匹配时确定匹配片断的边界也是基于实例机器翻译的难点之一,在这方面也采取了相应的解决方法。没有对翻译句子的连接装配进行更深入的研究,这是因为该翻译策略是用于多翻译引擎系统的,它要与其它翻译策略配合使用,以提高翻译结果的正确率。基于实例的机器翻译需要大量的双语语料库作为翻译时的依据,而人工建设大型语料库费时费力,所以尝试采用计算机进行汉英双语语料库的自动建立,包括篇章对齐和单词级的对齐。     

以机器翻译技术为核心的多语信息处理研究

该文介绍了哈尔滨工业大学教育部-微软语言语音重点实验室在多语信息处理方面的研究进展和成果.首先综述了国内外的研究现状,然后重点介绍在统计机器翻译、机器翻译应用、机器翻译评价、跨语言信息检索等方面的研究工作.     

Novel algorithm machine translation for language translation tool

Fuzzy matching techniques are the presently used methods in translating the words. Neural machine translation and statistical machine translation are the methods used in MT. In machine translator tool, the strategy employed for translation needs to handle large amount of datasets and therefore the performance in retrieving correct matching output can be affected. In order to improve the matching score of MT, the advanced techniques can be presented by modifying the existing fuzzy based translator and neural machine translator. The conventional process of modifying architectures and encoding schemes are tedious process. Similarly, the preprocessing of datasets also involves more time consumption and memory utilization. In this article, a new spider web based searching enhanced translation is presented to be employed with the neural machine translator. The proposed scheme enables deep searching of available dataset to detect the accurate matching result. In addition, the quality of translation is improved by presenting an optimal selection scheme for using the sentence matches in source augmentation. The matches retrieved using various matching scores are applied to an optimization algorithm. The source augmentation using optimal retrieved matches increases the translation quality. Further, the selection of optimal match combination helps to reduce time requirement, since it is not necessary to test all retrieved matches in finding target sentence. The performance of translation is validated by measuring the quality of translation using BLEU and METEOR scores. These two scores can be achieved for the TA-EN language pairs in different configurations of about 92% and 86%, correspondingly. The results are evaluated and compared with other available NMT methods to validate the work.     

Pivot language approach for phrase-based statistical machine translation

This paper proposes a novel method for phrase-based statistical machine translation based on the use of a pivot language. To translate between languages L _s and L _t with limited bilingual resources, we bring in a third language, L _p , called the pivot language. For the language pairs L _s  − L _p and L _p  − L _t , there exist large bilingual corpora. Using only L _s  − L _p and L _p  − L _t bilingual corpora, we can build a translation model for L _s  − L _t . The advantage of this method lies in the fact that we can perform translation between L _s and L _t even if there is no bilingual corpus available for this language pair. Using BLEU as a metric, our pivot language approach significantly outperforms the standard model trained on a small bilingual corpus. Moreover, with a small L _s  − L _t bilingual corpus available, our method can further improve translation quality by using the additional L _s  − L _p and L _p  − L _t bilingual corpora.     

汉英机器翻译中基于实例的歧义结构消解

歧义是自然语言特别是汉语的显著特点和普遍现象,也是当前汉英机器翻译系统的主要处理难点之一。通过对其中一些常见汉语歧义结构的分析,提出一种基于实例的歧义结构消解方法。由于歧义结构的对应实例具有较高的“结构”上的代表性,通过与这些实例的相似性比较可以较准确地把握待消歧语段的内部结构。     

Recent developments in machine translation

This paper describes developments in the area of machine translation (MT). First, the paper gives an overview of developments in Germany in general; then, special problems are discussed. The system taken as an example is METAL (Machine Translation and Analysis of Natural Language), where recent development work has centered around two main topics. (i) Efforts have been made to make the system really multilingual. The German-to-English prototype had to be expanded, some system components had to be readjusted, and additional problems had to be solved. Currently, analysis and synthesis components for German, English, French, Spanish, and Dutch are under development. All these languages use a common system kernel and a standard interface structure. (ii) The system had to be made user-friendly. This was an even more important task as, up to now, MT systems have not been well accepted by users. METAL tries to be more realistic, and also tries to support the main user interfaces in a much better way than has been done before. This is based on the conviction that there are several parameters which determine the real success of an MT system. It is not just translation quality which is decisive, it is also the integration of an MT system into the whole process of preparing and translating documents.Gregor Thurmair is head of the Linguistics Department at Siemens Nixdorf Information Systems and project leader of the machine translation group, METAL. He is involved in projects in information retrieval (morphological analysis), speech understanding (parsing, semantics) and machine translation (METAL system). He has presented papers on morphology, semantics in speech understanding, transfer problems in MT, and grammar checking.     

Virtual machine tools and virtual machining—A technological review

Virtual manufacturing systems provide a useful means for products to be manufactured ‘right the first time’ without the need of physical testing on the shop floor. Earlier research was mostly on developing a virtual manufacturing environment. Over the years, simple graphical prediction and simulation gave way to complex multi-science predictions. Virtual systems such as Virtual Machine Tool, Virtual Machining, Virtual Assembly, Virtual Tooling and Virtual Prototype have been developed to support virtual manufacturing. Different systems and approaches have different targeted applications. This paper aims to provide a comprehensive review of existing virtual systems. Their focuses and approaches (i.e. virtual reality, Web-based techniques, mathematical modelling, hardware interactions and STEP-NC-based methodologies) are discussed in detail. To better understand the systems, we have categorized them into different groups according to their application domains. Discussions and concluding remarks are given based on the review.     

The parallel corpus for information extraction based on natural language processing and machine translation

In conventional algorithms, the lack of entity information, reference, and semantic relations in the current corpus leads to a low rate of precision and efficiency in constructing cross‐language bilingual mapping. According to natural language processing and machine translation technology, to solve the problem, this paper aims to establish a parallel corpus for information extraction based on the OntoNotes corpus by combining automatic extraction and manual adjustment. To verify the validity of the parallel corpus constructed in this paper, a comparative experiment was carried out on the corpus. The corpus entity alignment rate, anaphora absence, and syntactic structure were analysed in detail based on statistics. The data set is well performed in language processing and machine translation. The parallel corpus for information extraction constructed in this paper can produce highly precise, stable, and efficient information in the process of bilingual mapping, which provides an effective parallel corpus for the study in machine translation of bilingual mapping.     

A portable virtual machine for Ada

A portable compiler can be constructed by letting it generate code for a virtual machine, which is then implemented on the real target machines. The design of a virtual machine which is especially suitable as a target machine for compiled Ada programs is described. The main design goals, implementability on mini-computers and portability, are discussed and the resulting design is described in some detail. Some implementation strategies for the machine are proposed and the feasibility of the virtual machine approach is discussed.     

机器翻译系统及翻译工具的开发和使用

本文综述了当前计算机翻译软件的需求和应用情况,着重讨论了如何设计翻译系统使其质量能达到可出版水平, 其中包括开发受限语言翻译系统, 翻译工作站, 及软件本地化。同时本文还涉及到如何开发非传统概念的翻译软件,特别是针对Web页面和其它应用在Internet上各类信息的翻译软件。本文还讨论了机器翻译未来的需要以及正在开发的一些系统。文中最后一部分比较了一下通过人译、机译,机助人译的方式达到的几种最合适的翻译方法。     

基于短语统计翻译的汉维机器翻译系统

描述了一种基于短语统计翻译的汉维机器翻译系统。首先使用汉维语料进行训练,得到语言模型和翻译模型;再利用训练好的模型对源语句进行解码,以得到最佳的翻译语句。解码的核心算法是柱搜索（beam search）算法。其中维文语料使用的是拉丁维文。实验结果表明,基于短语的统计机器翻译方法可以快速有效地构建一个汉维机器翻译平台。     

跨平台系统虚拟机的二进制翻译优化

在跨平台系统虚拟机原型ARCH-BRIDGE的基础上,提出了一种基本块级的动态二进制翻译优化方法,通过两阶段翻译、基于虚拟寄存器的优化翻译及延迟机器状态提交等技术,可在不采用中间表示及确保精确异常的情况下,有效去除二进制翻译所引入的冗余。测试表明,优化后的ARCH-BRIDGE在翻译开销明显优于QEMU的同时,翻译块尺寸和翻译冗余得到了大幅降低,并且SPEC CPU2006、NBENCH及OS引导的性能均得到了显著提升。     

基于模板的汉英专利机器翻译研究

为了改善专利文献的机器翻译效果, 提出了一种基于模板的机器翻译方法。通过分析汉英双语对齐的专利文献语料, 人工书写了600余条模板, 模板的设计思想源于弱化的正则文法; 设计和实现了一个模板翻译模块, 并将其与已有的一个基于规则的机器翻译系统相融合。实验结果表明, 40%以上的测试文本的翻译质量得到了提高, 因此, 这种基于模板的方法对于改善专利文献的机器翻译效果是有效的。     

基于LSTM的蒙汉机器翻译的研究

由于内蒙古地区蒙汉机器翻译水平落后、平行双语语料规模较小,利用传统的统计机器翻译方法会出现数据稀疏以及训练过拟合等问题,导致翻译质量不高。针对这种情况,提出基于LSTM的蒙汉神经机器翻译方法,通过利用长短时记忆模型构建端到端的神经网络框架并对蒙汉机器翻译系统进行建模。为了更有效地理解蒙古语语义信息,根据蒙古语的特点将蒙古文单词分割成词素形式,导入模型,并在模型中引入局部注意力机制计算与目标词有关联的源语词素的权重,获得蒙古语和汉语词汇间的对齐概率,从而提升翻译质量。实验结果表明,该方法相比传统蒙汉翻译系统提高了翻译质量。     

基于协作型VMM的虚拟机执行环境动态配置模型

针对当前各类虚拟机监控器(VMM)在定制虚拟机执行环境过程中灵活性不足、可用性不强方面的问题,提出并设计了一种用户动态配置虚拟机执行环境的模型,并在协作型VMM之上进行了实现。结合Intel VT-x技术的实现机制,充分利用虚拟机控制结构(VMCS)中的执行控制域特性,通过为用户提供接口,对虚拟机配置文件进行操作,实现对虚拟机执行环境的实时动态配置。用户利用该模型能够快速构建具有不同运行时特性的虚拟机执行环境。测试结果表明,该模型能够提高VMM的可用性。     

统计机器翻译中翻译规则抽取

对齐短语是决定统计机器翻译系统质量的核心模块。提出基于短语结构树的层次短语模型,这是利用串-树模型的思想对层次短语模型的扩展。基于短语结构树的层次短语模型是在双语对齐短语的基础之上结合英语短语结构树抽取翻译规则,并利用启发式策略获得翻译规则的扩展句法标记。采用翻译规则的统计机器翻译系统在不同数据集上具有稳定的翻译结果,在训练集和测试集的平均BlEU评分高于短语模型和层次短语模型的BLEU评分。     

Task mapper and application-aware virtual machine scheduler oriented for parallel computing

We design a task mapper TPCM for assigning tasks to virtual machines, and an application-aware virtual machine scheduler TPCS oriented for parallel computing to achieve a high performance in virtual computing systems. To solve the problem of mapping tasks to virtual machines, a virtual machine mapping algorithm (VMMA) in TPCM is presented to achieve load balance in a cluster. Based on such mapping results, TPCS is constructed including three components: a middleware supporting an application-driven scheduling, a device driver in the guest OS kernel, and a virtual machine scheduling algorithm. These components are implemented in the user space, guest OS, and the CPU virtualization subsystem of the Xen hypervisor, respectively. In TPCS, the progress statuses of tasks are transmitted to the underlying kernel from the user space, thus enabling virtual machine scheduling policy to schedule based on the progress of tasks. This policy aims to exchange completion time of tasks for resource utilization. Experimental results show that TPCM can mine the parallelism among tasks to implement the mapping from tasks to virtual machines based on the relations among subtasks. The TPCS scheduler can complete the tasks in a shorter time than can Credit and other schedulers, because it uses task progress to ensure that the tasks in virtual machines complete simultaneously, thereby reducing the time spent in pending, synchronization, communication, and switching. Therefore, parallel tasks can collaborate with each other to achieve higher resource utilization and lower overheads. We conclude that the TPCS scheduler can overcome the shortcomings of present algorithms in perceiving the progress of tasks, making it better than schedulers currently used in parallel computing.     

基于虚拟机技术的DCS仿真系统设计与实现

提出了基于虚拟机技术的DCS仿真系统的实现方式,描述了虚拟控制器的具体实现方法及虚拟机技术的其他应用.