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Chan Sin-Wai (ed): The human factor in machine translation 陈善伟:机器翻译中的人为因素
IF 1.9 Q1 Arts and Humanities Pub Date : 2019-09-01 DOI: 10.1007/s10590-019-09240-x
Sheila Castilho
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引用次数: 1
Ruslan Mitkov, Johanna Monti, Gloria Corpas Pastor, and Violeta Seretan (eds): Multiword units in machine translation and translation technology Ruslan Mitkov, Johanna Monti, Gloria Corpas Pastor和Violeta Seretan(编):机器翻译和翻译技术中的多词单元
IF 1.9 Q1 Arts and Humanities Pub Date : 2019-08-28 DOI: 10.1007/s10590-019-09239-4
Rejwanul Haque, Mohammed Hasanuzzaman, Andy Way
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引用次数: 2
Towards an open platform for machine translation of spoken languages into sign languages 建立一个开放的平台,将口语翻译成手语
IF 1.9 Q1 Arts and Humanities Pub Date : 2019-08-27 DOI: 10.1007/s10590-019-09238-5
Rostand E. O. Costa, Tiago M. U. de Araújo, M. A. C. B. Lima, Vinícius Veríssimo, Renan S. de Andrade, Suanny Vieira, Alexandre Santos, Guido L. Souza Filho, Marília K. Soares, Vitor Hanael
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引用次数: 3
Improving Arabic neural machine translation via n-best list re-ranking 通过n最佳列表重新链接改进阿拉伯语神经机器翻译翻译
IF 1.9 Q1 Arts and Humanities Pub Date : 2019-08-26 DOI: 10.1007/s10590-019-09237-6
Mohamed Seghir Hadj Ameur, A. Guessoum, F. Meziane
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引用次数: 6
Evaluation of the impact of controlled language on neural machine translation compared to other MT architectures 对比其他机器翻译架构,评估受控语言对神经机器翻译的影响
IF 1.9 Q1 Arts and Humanities Pub Date : 2019-05-31 DOI: 10.1007/s10590-019-09233-w
Shaimaa Marzouk, S. Hansen-Schirra
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引用次数: 15
Editors’ foreword to the special issue on human factors in neural machine translation 《神经机器翻译中的人为因素》特刊编者序
IF 1.9 Q1 Arts and Humanities Pub Date : 2019-05-02 DOI: 10.1007/s10590-019-09231-y
Sheila Castilho, F. Gaspari, Joss Moorkens, Maja Popovic, Antonio Toral
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引用次数: 5
Efficient document alignment across scenarios 跨场景的高效文档对齐
IF 1.9 Q1 Arts and Humanities Pub Date : 2019-04-24 DOI: 10.1007/s10590-019-09234-9
Andoni Azpeitia, T. Etchegoyhen
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引用次数: 6
An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach 深入分析受控语言规则对机器翻译输出的个体影响:一种混合方法
IF 1.9 Q1 Arts and Humanities Pub Date : 2019-04-22 DOI: 10.1007/s10590-021-09266-0
Shaimaa Marzouk
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引用次数: 7
Multi-modal indicators for estimating perceived cognitive load in post-editing of machine translation 机器翻译后编辑感知认知负荷的多模态指标估计
IF 1.9 Q1 Arts and Humanities Pub Date : 2019-03-15 DOI: 10.1007/s10590-019-09227-8
Nico Herbig, Santanu Pal, M. Vela, A. Krüger, Josef van Genabith
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引用次数: 21
An error analysis for image-based multi-modal neural machine translation. 基于图像的多模态神经机器翻译的误差分析。
IF 1.9 Q1 Arts and Humanities Pub Date : 2019-01-01 Epub Date: 2019-04-08 DOI: 10.1007/s10590-019-09226-9
Iacer Calixto, Qun Liu

In this article, we conduct an extensive quantitative error analysis of different multi-modal neural machine translation (MNMT) models which integrate visual features into different parts of both the encoder and the decoder. We investigate the scenario where models are trained on an in-domain training data set of parallel sentence pairs with images. We analyse two different types of MNMT models, that use global and local image features: the latter encode an image globally, i.e. there is one feature vector representing an entire image, whereas the former encode spatial information, i.e. there are multiple feature vectors, each encoding different portions of the image. We conduct an error analysis of translations generated by different MNMT models as well as text-only baselines, where we study how multi-modal models compare when translating both visual and non-visual terms. In general, we find that the additional multi-modal signals consistently improve translations, even more so when using simpler MNMT models that use global visual features. We also find that not only translations of terms with a strong visual connotation are improved, but almost all kinds of errors decreased when using multi-modal models.

在这篇文章中,我们对不同的多模态神经机器翻译(MNMT)模型进行了广泛的定量误差分析,这些模型将视觉特征集成到编码器和解码器的不同部分。我们研究了在具有图像的平行句子对的域内训练数据集上训练模型的场景。我们分析了两种不同类型的MNMT模型,它们使用全局和局部图像特征:后者对图像进行全局编码,即有一个特征向量表示整个图像,而前者对空间信息进行编码,即存在多个特征向量,每个特征向量对图像的不同部分进行编码。我们对不同的MNMT模型以及纯文本基线生成的翻译进行了错误分析,研究了在翻译视觉和非视觉术语时多模态模型的比较。总的来说,我们发现额外的多模态信号持续地改善翻译,当使用使用全局视觉特征的更简单的MNMT模型时更是如此。我们还发现,在使用多模态模型时,不仅具有强烈视觉内涵的术语的翻译得到了改善,而且几乎所有类型的错误都减少了。
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引用次数: 9
期刊
MACHINE TRANSLATION
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