从计算机辅助翻译到协同翻译

由于机器翻译系统的译文质量仍难以达到实用化要求,计算机辅助翻译技术逐渐成为研究热点,并且取得了很好的实际效果,大大提高了翻译产业的生产率。随着辅助翻译规模的不断扩大,多名在空间上分散的用户被组织起来共同完成一项翻译任务已成为普遍现象,这种新的翻译模式称为协同翻译。该文对计算机辅助翻译和协同翻译技术进行综述,首先从辅助译文生成、译后编辑和系统反馈学习等方面介绍了计算机辅助翻译技术的常用方法和研究进展,随后讨论了计算机辅助翻译与协同翻译之间的联系和区别,分析了协同翻译技术所面临的主要问题,并介绍了现有研究的解决方法。最后对协同翻译的未来发展方向进行了展望。     

人工智能技术在计算机辅助翻译软件中的应用

在计算机翻译行业中,利用神经网络体系引入深度学习模式,并建立完整的数据库信息,实现了高质量和高效率的文本翻译。目前,我国计算机辅助翻译系统开发中引入了人工智能技术,虽说取得了一定的研究成果,但依然存在翻译软件功能较为单一和翻译精准度有限等问题。为了提升译文精准度和丰富翻译软件的功能,基于人工智能技术设计了计算机辅助翻译系统,并对其性能进行测试。     

基于用户行为模型的计算机辅助翻译方法

与全自动机器翻译相比,计算机辅助翻译技术更具实用性,已成为机器翻译领域的一个研究热点.传统的辅助翻译过程中,用户只能被动接受系统提供的辅助译文,并进行翻译后编辑操作.该文提出一种基于用户行为模型的辅助翻译方法,通过实时记录用户的后编辑过程,分析出用户的翻译决策,建立用户行为模型,使得翻译系统能够动态获取和共享用户的翻译...     

国内外四种常见计算机辅助翻译软件比较研究

计算机辅助翻译软件能够对翻译的效率和质量有着明显提高的作用,但是在当下各种计算机辅助翻译软件层出不穷的情况下,使用者想要选择到一个与自身翻译需求相符合的计算机辅助翻译软件是比较困难的。本文就对国内外常见的四种计算机辅助翻译软件进行了比较,以期提供参考。     

计算机辅助翻译教学的应用及改进

全球化背景下,计算机辅助翻译是目前最流行的翻译趋势,普及并应用计算机辅助翻译技术是目前高校外语专业需要重点关注的问题.本文以成都理工大学师生为研究对象,对成都理工大学计算机辅助翻译技术教学现状进行了调查,在此基础上通过分析计算机辅助翻译教学的实践价值,提出了辅助翻译技术教学改进策略,旨在为有关教育工作者提供参考意见.     

利用句法信息改进交互式机器翻译

在很多领域中,全自动机器翻译的译文质量还无法达到令人满意的程度。要想获得正确无误的译文,往往需要翻译人员对自动翻译系统的输出进行后处理。在交互式机器翻译的框架内,翻译系统和译员协同工作,译员确认系统提供的译文中的最长正确前缀,系统据此对译文后缀进行预测,共同完成翻译任务。该文利用基于短语的翻译模型,建立了交互式机器翻译系统,并结合交互式机器翻译的特点,利用句法层面的子树信息来指导翻译假设的扩展。实验表明,该方法可以有效地减少人机交互次数。     

基于动态词对齐的交互式机器翻译

在传统的机器翻译(machine translation,MT)与计算机辅助翻译(computer aided translation,CAT)中,译员与翻译引擎之间的交互受到很大限制,于是产生了交互式机器翻译(interactive machine translation,IMT)技术。但传统的模型只考虑当前源语与部分翻译的目标语的信息,没有将用户交互后的对齐信息加入到之后的预测模型中。该文基于词预测交互式机器翻译的研究思路,将用户交互翻译过程中的鼠标点选行为转化为中间译文的词对齐信息,进而在翻译交互过程中实现了对译文的动态词对齐标注,并在词对齐信息和输入译文的约束下提高了传统词预测的准确性。     

关于计算机辅助翻译的研究学习

第三次科技革命后,世界各国技术飞速进步,翻译界逐步发生变革。在人工翻译成本较高、效率有待提升的情况下,基于实例与基于语料库的机器翻译应运而生。随着计算机的普及及操作系统的不断增强,计算机辅助翻译的水平与可操作性大幅提高。基于此,从科技文本、商务文本以及文学文本三方面,例证分析计算机辅助翻译的实际运用,探讨计算机辅助翻译的发展前景。     

计算机翻译技术在非技术文本翻译中的应用

为了找到一个更加适合非技术文本翻译的翻译技术,提出了CAT+MT翻译技术研究.该项技术以MT翻译为初稿,利用CAT技术纠正译文,以此提高译文精准度.为了实现此功能,利用翻译软件构建了翻译系统框架结构,通过调用CAT数据库,完成非技术文本翻译.质量评估结果显示,CAT+MT翻译方案,读者反馈较好,并且出错率仅有0.63％...     

利用统计搭配模型改进基于实例的机器翻译

基于实例的机器翻译(example-based machine translation,简称EBMT)使用预处理过的双语例句作为主要翻译资源,通过编辑与待翻译句子匹配的翻译实例来生成译文.在EBMT系统中,翻译实例选择及译文选择对系统性能影响较大.提出利用统计搭配模型来增强EBMT系统中翻译实例选择及译文选择的能力,提高译文质量.首先,使用单语统计词对齐从单语语料中训练统计搭配模型.然后,利用该模型从3个方面提高EBMT的性能:(1)利用统计搭配模型估计待翻译句子与翻译实例之间的匹配度,从而增强系统的翻译实例选择能力;(2)通过引入候选译文与上下文之间搭配强度的估计来提高译文选择能力;(3)使用统计搭配模型检测翻译实例中被替换词的搭配词,同时根据新的替换词及上下文对搭配词进行矫正,进一步提高EBMT系统的译文质量.为了验证所提出的方法,在基于词的EBMT系统上评价了英汉翻译的译文质量.与基线系统相比,所提出的方法使译文的BLEU得分提高了4.73～6.48个百分点.在半结构化的EBMT系统上进一步检验了基于统计搭配模型的译文选择方法,从实验结果来看,该方法使译文的BLEU得分提高了1.82个百分点.同时,人工评价结果显示,改进后的半结构化EBMT系统的译文能够表达原文的大部分信息,并且具有较高的流利度.     

Computer-assisted translation using speech recognition

Current machine translation systems are far from being perfect. However, such systems can be used in computer-assisted translation to increase the productivity of the (human) translation process. The idea is to use a text-to-text translation system to produce portions of target language text that can be accepted or amended by a human translator using text or speech. These user-validated portions are then used by the text-to-text translation system to produce further, hopefully improved suggestions. There are different alternatives of using speech in a computer-assisted translation system: From pure dictated translation to simple determination of acceptable partial translations by reading parts of the suggestions made by the system. In all the cases, information from the text to be translated can be used to constrain the speech decoding search space. While pure dictation seems to be among the most attractive settings, unfortunately perfect speech decoding does not seem possible with the current speech processing technology and human error-correcting would still be required. Therefore, approaches that allow for higher speech recognition accuracy by using increasingly constrained models in the speech recognition process are explored here. All these approaches are presented under the statistical framework. Empirical results support the potential usefulness of using speech within the computer-assisted translation paradigm.     

Translation project adaptation for MT-enhanced computer assisted translation

The effective integration of MT technology into computer-assisted translation tools is a challenging topic both for academic research and the translation industry. In particular, professional translators consider the ability of MT systems to adapt to the feedback provided by them to be crucial. In this paper, we propose an adaptation scheme to tune a statistical MT system to a translation project using small amounts of post-edited texts, like those generated by a single user in even just one day of work. The same scheme can be applied on a larger scale in order to focus general purpose models towards the specific domain of interest. We assess our method on two domains, namely information technology and legal, and four translation directions, from English to French, Italian, Spanish and German. The main outcome is that our adaptation strategy can be very effective provided that the seed data used for adaptation is ‘close enough’ to the remaining text to be translated; otherwise, MT quality neither improves nor worsens, thus showing the robustness of our method.     

基于动词配价的德汉译文转换生成的研究

本文对德汉机译系统中,动词配价理论在译文转换生成中的应用进行了分析,结合实际的系统为基础,阐述了以译文转换式为主、以语料规则为辅的转换生成方法,并讨论了语义要素的具体实现。     

Improving on-line handwritten recognition in interactive machine translation

On-line handwriting text recognition (HTR) could be used as a more natural way of interaction in many interactive applications. However, current HTR technology is far from developing error-free systems and, consequently, its use in many applications is limited. Despite this, there are many scenarios, as in the correction of the errors of fully-automatic systems using HTR in a post-editing step, in which the information from the specific task allows to constrain the search and therefore to improve the HTR accuracy. For example, in machine translation (MT), the on-line HTR system can also be used to correct translation errors. The HTR can take advantage of information from the translation problem such as the source sentence that is translated, the portion of the translated sentence that has been supervised by the human, or the translation error to be amended. Empirical experimentation suggests that this is a valuable information to improve the robustness of the on-line HTR system achieving remarkable results.     

Simulation of English translation text filtering based on machine learning and embedded system

The translation is generally assumed to distinguish it from the original language text in the same area. Therefore, say these constitute a unique multilingual translation, commonly referred to as translation; translation is also affected by the source language and exhibits different characteristics according to the source language. Therefore, claim that these variants constitute the same target language translated into additional "dialog." Using Machine Learning English and embedded technologies investigated the differences between the general characteristics of different translation sources and language translation. There is little research corpus between the complicated relationship between translation and the original text and translation itself for very different language types. May does not translate enough knowledge of the subject areas covered. There are many guidelines to help authors clearly express their ideas to promote translation; scientists and engineers find it difficult to apply the procedures required for a high degree of speech recognition. For financial reasons, non-essential text may be edited publication. In this case, as described in the article, the author can take some precautions to express terms of intelligibility. Machine Learning and embedded systems determine the importance of various features for data collection and the importance of its characteristics compared with previously reported studies in English. Also, the method allows us to add the grammatical function of translation studies rarely used. The results show that, even if only based on five features, the full translation can be reliably separated from the non-translated region.     

基于跨模态实体信息融合的神经机器翻译方法

现有多模态机器翻译(Multi-modal machine translation, MMT)方法将图片与待翻译文本进行句子级别的语义融合. 这些方法存在视觉信息作用不明确和模型对视觉信息不敏感等问题, 并进一步造成了视觉信息与文本信息无法在翻译模型中充分融合语义的问题. 针对这些问题, 提出了一种跨模态实体重构(Cross-modal entity reconstruction, CER)方法. 区别于将完整的图片输入到翻译模型中, 该方法显式对齐文本与图像中的实体, 通过文本上下文与一种模态的实体的组合来重构另一种模态的实体, 最终达到实体级的跨模态语义融合的目的, 通过多任务学习方法将CER模型与翻译模型结合, 达到提升翻译质量的目的. 该方法在多模态翻译数据集的两个语言对上取得了最佳的翻译准确率. 进一步的分析实验表明, 该方法能够有效提升模型在翻译过程中对源端文本实体的忠实度.     

Simultaneous translation of lectures and speeches

With increasing globalization, communication across language and cultural boundaries is becoming an essential requirement of doing business, delivering education, and providing public services. Due to the considerable cost of human translation services, only a small fraction of text documents and an even smaller percentage of spoken encounters, such as international meetings and conferences, are translated, with most resorting to the use of a common language (e.g. English) or not taking place at all. Technology may provide a potentially revolutionary way out if real-time, domain-independent, simultaneous speech translation can be realized. In this paper, we present a simultaneous speech translation system based on statistical recognition and translation technology. We discuss the technology, various system improvements and propose mechanisms for user-friendly delivery of the result. Over extensive component and end-to-end system evaluations and comparisons with human translation performance, we conclude that machines can already deliver comprehensible simultaneous translation output. Moreover, while machine performance is affected by recognition errors (and thus can be improved), human performance is limited by the cognitive challenge of performing the task in real time.     

A system for terminology extraction and translation equivalent detection in real time

In this paper we present a system for automatic terminology extraction and automatic detection of the equivalent terms in the target language to be used alongside a computer assisted translation (CAT) tool that provides term candidates and their translations in an automatic way each time the translator goes from one segment to the next one. The system uses several sources of information: the text from the segment being translated and from the whole translation project, the translation memories assigned to the project and a translation phrase table from a statistical machine translation system. It also uses the terminological database assigned to the project in order to avoid presenting already known terms. The use of translation phrase tables allows us to use very large parallel corpora in a very efficient way. We have used Moses to calculate and to consult the translation phrase tables. The program is written in Python and it can be used with any CAT tool. In our experiments we have used OmegaT, a well-known open source CAT tool. Evaluation results for English–Spanish and for three subjects (politics, finance, and medicine) are presented.