Editors’ foreword to the special issue on human factors in neural machine translation
Editors’ foreword to the special issue on human factors in neural machine translation
复制标题
神经机器翻译中人为因素特刊的编辑前言
作者：
Sheila Castilho;F. Gaspari;Joss Moorkens;Maja Popovic;Antonio Toral
在过去的5年里，机器翻译（MT）社区已经意识到神经机器翻译（NMT）的潜力，以维持在使用统计MT（SMT）时出现平台的输出质量的提高（Kenny 2018）。这使得越来越多的MT供应商和研究小组将精力和资源集中在开发NMT系统上。关于NMT质量的早期研究表明，一般来说，这种MT范式产生的自动评估指标得分高于其前身SMT（Bahdanau et al. 2014; Jean et al. 2015; Bojar et al. 2016; Koehn and Knowles 2017）。与SMT相比，NMT也显示出流畅性的跳跃（Bentivogli et al. 2016; Toral and Sánchez-Cartagena 2017）。这种增加的流畅性迅速使NMT成为同化的首选MT模式，许多主要的在线MT提供商转向NMT就是明证。就用于传播的机器翻译而言，当文本被“机器翻译作为生产的中间步骤”时（Forcada 2010），我们可以合理地假设，所报告的质量提高将导致生产力的相应提高。然而，Castilho等人（2018）等研究报告称，与基于短语的SMT（PBSMT）系统相比，相对于使用自动指标和人类流畅性评估的改进分数，NMT在生产力和技术工作方面仅略有改进。Way（2018）提出的MT部署的经验法则是，“在特定的翻译场景中，所需或所需的人工参与程度将取决于内容的目的、价值和保质期。然而，对NMT同化作用的积极评价以及媒体中偶尔的双曲线报道（如Castilho等人2017年; Toral等人2018年所报告），
Over the past 5 years the machine translation (MT) community has become aware of the potential of neural machine translation (NMT) to sustain the increases in output quality that had appeared to plateau when using statistical MT (SMT) (Kenny 2018). This has led an increasing number of MT providers and research groups to focus their energies and resources on developing NMT systems. Early studies on NMT quality demonstrated that, in general, this MT paradigm yields higher automatic evaluation metric scores than its predecessor, SMT (Bahdanau et al. 2014; Jean et al. 2015; Bojar et al. 2016; Koehn and Knowles 2017). NMT has also been shown to provide a jump in fluency when compared with SMT (Bentivogli et al. 2016; Toral and Sánchez-Cartagena 2017). This increased fluency has quickly made NMT the preferred MT paradigm for assimilation, as is evident from the move to NMT by many major online MT providers. Where MT for dissemination is concerned, when text is “machine translated as an intermediate step in production” (Forcada 2010), we might reasonably assume that the reported increase in quality would result in a concomitant productivity boost. However, studies such as Castilho et al. (2018) reported that NMT delivers only minor improvements in productivity and technical effort, relative to the improved scores using automatic metrics and human fluency evaluation, when comparing with phrase-based SMT (PBSMT) systems. The rule of thumb for MT deployment suggested by Way (2018) is that “the degree of human involvement required—or warranted—in a particular translation scenario will depend on the purpose, value and shelf-life of the content.” However, positive evaluations of NMT for assimilation alongside occasionally hyperbolic reports in the media (as reported in Castilho et al. 2017; Toral et al. 2018) have