共查询到19条相似文献,搜索用时 156 毫秒
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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。 相似文献
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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。 相似文献
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统计机器翻译从诞生至今获得了长足的发展,目前已经成为机器翻译的主流.但是作为基础模块之一的翻译模型却随训练语料的增大而呈现飞速增大的趋势.为了使统计机器翻译更加实用,翻译模型的约简一直是研究热点之一.概述了统计机器翻译中翻译模型约简的研究现状,相关方法主要围绕解码过程统计分析、训练语料中的统计分析、翻译模型中的短语对自身特点分析等三个类别.结合相关分析,最后也探讨了这个方向的未来发展趋势. 相似文献
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余正红伍永豪邓娟王俊 《计算机与数字工程》2014,(2):239-242,260
针对传统机器翻译系统准确性差、人工翻译成本高等缺陷,提出了一种基于Hadoop云计算框架与XMPP协议的云翻译系统解决方案,结合传统翻译技术和Hadoop云计算框架,利用XMPP在异构系统之间的互通,建立用户、译员和对象的三方互助云平台.该系统可挖掘互助沟通过程中的庞杂的语料资源,具有语料库数据量大,翻译准确、翻译效率高、智能性强等特点,解决了人工翻译成本高、机器翻译歧义性大等问题,实现了不同语种人群通过互联网进行文字即时通信时的多语无障碍沟通. 相似文献
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近年来,为了提高统计机器翻译系统的准确性,普遍应用海量语料训练出大规模语言模型和翻译模型.而模型规模的不断增大,给统计机器翻译带来了突出的计算性能问题,使得现有的单机串行化翻译处理难以在较快的时间内完成计算,该问题在处理联机翻译时更为突出.为了克服单机机器翻译算法在这方面的局限性,提高大规模统计机器翻译处理的计算性能,面向一个实际的联机翻译系统,提出了一个分布式和并行化翻译解码算法框架,对整个大规模语言模型和翻译模型同时采用分布式存储和并行化查询机制,在此基础上进一步研究实现完整的翻译解码并行化算法.研究实现了一个基于分布式内存数据库的层次短语并行化机器翻译解码器,该解码器使用分布式内存数据库存储和查询大数据量的翻译模型表和语言模型表,克服了传统的机器翻译系统所面临的内存容量和并发度方面的限制.为了进一步提高并行解码速度,还研究实现了另外3项优化技术:1)将翻译模型表的同步规则和Trie树结构的语言模型表转化为基于内存数据库的"键-值"结构的Hash索引表的方法;2)对Cube-Pruning算法进行了修改使其适用于批量查询;3)采用并优化了批量查询方式减少语言和翻译模型查询时的网络传输开销.所提出的解码算法实现了基于大规模语料统计机器翻译时的快速解码,并具备优异的系统可扩展性.实验结果表明:与单机解码器相比,单句翻译速度可提高2.7倍,批量翻译作业的总体解码性能可提高至少11.7倍,实现了显著的计算性能提升. 相似文献
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一种智能译后编辑器的设计及其实现算法 总被引:4,自引:0,他引:4
译后编辑是改进机器翻译译文质量的主要手段.本文提出一个智能译后编辑器的设计原理和实现算法.该编辑器以意段为基本处理单位,既可以形成适于反向推理的译后编辑反馈信息,为机译系统知识的自完善提供处理依据,又可以实现源译文句子级和意段级的多窗口同步显示.同时还利用智能机译系统对句子/短语的多解译文和单词的多义查询能力,使用户只要在误译文的多个候选译文中选择正确译文,从而大量减少人工删除误译文和插入正确译文的操作,并通过设置多个意段译文位置的自动调整机制,提高译后编辑的效率. 相似文献
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《Displays》2023
The technology for converting Chinese to Braille is of great importance. When paired with a Braille display, it can better meet the educational and daily needs of the visually impaired community, especially children and students. Incorporating visual assistance mechanisms can further enhance the user experience and provide comprehensive support for individuals with visual impairments. In recent years, the use of end-to-end neural machine translation models for Chinese–Braille translation has gained traction. However, this task requires large, high-quality, and domain-specific parallel data to train robust models. Unfortunately, the existing Chinese–Braille parallel data is insufficient to achieve satisfactory results. To address this challenge, this paper puts forward a groundbreaking approach that integrates pre-training models into the Chinese Braille translation task. This represents the first-ever application of such technology in this context and it is different from traditional pre-training methods. While previous pre-training method of natural language processing mainly utilized raw text data, we have identified its limitations in improving Chinese–Braille translation. Therefore, we have proposed three novel forms of pre-training datasets, instead of relying solely on raw text data. By utilizing the Transformer model, our approach achieves the highest BLEU score of 94.53 on a 10k parallel corpus, presenting a new direction for Chinese–Braille translation research. Furthermore, we introduce a new form of data that enables Chinese–Braille translation solely using the encoder framework. Leveraging the MacBERT model, this approach achieves a BLEU score of 98.87 on the test set and demonstrates an inference speed 54 times faster than the Transformer model. These findings have significant implications for the field of Chinese–Braille translation, providing insights for future research endeavors. 相似文献
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杜鹏举 《自动化技术与应用》2021,40(2):167-169,185
计算机辅助翻译技术即基于计算机设备开展翻译工作,核心在于翻译记忆。计算机可就实际需要将已翻译原文与译文进行自动化存储,以此再次有相似与重复语句时,计算机便可自主匹配,有效降低反复翻译几率,保证翻译效率与质量,最大程度上满足不断增长的翻译需要。本文详细分析了计算机辅助翻译技术,设计了计算机辅助翻译流程与系统,并通过实验验证了技术的时效性,结果表明,计算机辅助翻译技术的译文BLEU值较高,准确率高达95%;计算机辅助翻译系统可动态化获得并分享翻译知识,以提升翻译效率与质量。 相似文献
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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. 相似文献
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针对神经机器翻译中资源稀缺的问题,提出了一种基于双向依存自注意力机制(Bi-Dependency)的依存句法知识融合方法。首先,利用外部解析器对源句子解析得到依存解析数据;然后,将依存解析数据转化为父词位置向量和子词权重矩阵;最后,将依存知识融合到Transformer编码器的多头注意力机制上。利用Bi-Dependency,翻译模型可以同时对父词到子词、子词到父词两个方向的依存信息进行关注。双向翻译的实验结果表明,与Transformer模型相比,在富资源情况下,所提方法在汉-泰翻译上的BLEU值分别提升了1.07和0.86,在汉-英翻译上的BLEU值分别提升了0.79和0.68;在低资源情况下,所提方法在汉-泰翻译上的BLEU值分别提升了0.51和1.06,在汉-英翻译上的BLEU值分别提升了1.04和0.40。可见Bi-Dependency为模型提供了更丰富的依存信息,能够有效提升翻译性能。 相似文献
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《Displays》2023
Transforming Mandarin Braille to Chinese text is a significant but less focused machine translation task. CBHG is a building block used in the Tacotron text-to-speech model. Since Mandarin Braille is constructed from the pronunciation of Chinese characters, CBHG can be used to perform Braille–Chinese translation. Unfortunately, only relying on the convolution blocks in CBHG cannot effectively extract the features of Braille sequences. Two ways are proposed to improve the CBHG model: CBHG-SE and CBHG-ECA. The two modules adaptively recalibrate channel-wise feature responses by explicitly modeling interdependencies between channels in CBHG. The quality of representations produced by the network can also be improved. Meanwhile, the network can learn to use global information to emphasize informative features and suppress less useful ones selectively. CBHG-ECA has stronger feature recalibration capabilities than CBHG-SE due to its more direct correspondence between channels and their weights. These two models can achieve 92.23 BLEU and 91.48 BLEU on the Braille–Chinese dataset, outperforming CBHG and other neural machine translation models. 相似文献
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The advantages of neural machine translation (NMT) have been extensively validated for offline translation of several language pairs for different domains of spoken and written language. However, research on interactive learning of NMT by adaptation to human post-edits has so far been confined to simulation experiments. We present the first user study on online adaptation of NMT to user post-edits in the domain of patent translation. Our study involves 29 human subjects (translation students) whose post-editing effort and translation quality were measured on about 4500 interactions of a human post-editor and an NMT system integrating an online adaptive learning algorithm. Our experimental results show a significant reduction in human post-editing effort due to online adaptation in NMT according to several evaluation metrics, including hTER, hBLEU, and KSMR. Furthermore, we found significant improvements in BLEU/TER between NMT outputs and professional translations in granted patents, providing further evidence for the advantages of online adaptive NMT in an interactive setup. 相似文献
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In this paper we present EXTRA (EXample-based TRanslation Assistant), a translation memory (TM) system. EXTRA is able to propose
effective translation suggestions by relying on syntactic analysis of the text and on a rigorous, language-independent measure;
the search is performed efficiently in large amounts of bilingual texts thanks to its advanced retrieval techniques. EXTRA
does not use external knowledge requiring the intervention of users and is completely customizable and portable as it has
been implemented on top of a standard DataBase Management System. The paper provides a thorough evaluation of both the effectiveness
and the efficiency of our system. In particular, in order to quantify the benefits offered by EXTRA assisted translation over
manual translation, we introduce a simulator implementing specifically devised statistical, process-oriented, discrete-event
models. As far as we know, this is the first time statistical simulation experiments have been used to face the nontrivial
problem of evaluating TM systems, particularly for comparing the time that could be saved by performing assisted translation
versus “manual” translation and for optimally tuning the system behaviour with respect to differently skilled users. In our
experiments, we considered three scenarios, manual translation with one or two translators and assisted translation with one
translator. The time needed for one translator to do an assisted translation is significantly closer to that of a team of
two translators than to that of the single translator. The mean sentence translation time is by far the lowest for this scenario,
corresponding to the highest per translator productivity. We also estimate the total translation time when the number of query
sentences, the maximum number of suggestions to be read, and the probability of look up are varied: the best trade-off is
given by reading (and presenting) four or five suggestions at the most. 相似文献