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Evaluation of the impact of controlled language on neural machine translation compared to other MT architectures

机译：与其他MT架构相比，评估受控语言对神经机器翻译的影响

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摘要

Many studies have shown that the application of controlled languages (CL) is an effective pre-editing technique to improve machine translation (MT) output. In this paper, we investigate whether this also holds true for neural machine translation (NMT). We compare the impact of applying nine CL rules on the quality of NMT output as opposed to that of rule-based, statistical, and hybrid MT by applying three methods: error annotation, human evaluation, and automatic evaluation. The analyzed data is a German corpus-based test suite of technical texts that have been translated into English by five MT systems (a neural, a rule-based, a statistical, and two hybrid MT systems). The comparison is conducted in terms of several quantitative parameters (number of errors, error types, quality ratings, and automatic evaluation metrics scores). The results show that CL rules positively affect rule-based, statistical, and hybrid MT systems. However, CL does not improve the results of the NMT system. The output of the neural system is mostly error-free both before and after CL application and has the highest quality in both scenarios among the analyzed MT systems showing a decrease in quality after applying the CL rules. The qualitative discussion of the NMT output sheds light on the problems that CL causes for this kind of MT architecture.
机译：许多研究表明，受控语言（CL）的应用是提高机器翻译（MT）输出的有效预编辑技术。在本文中，我们研究了神经机器翻译（NMT）是否也适用。我们通过应用三种方法（错误注释，人工评估和自动评估）比较了应用九种CL规则对NMT输出质量（与基于规则，统计和混合MT相比）的影响。分析的数据是基于德国语料库的技术文本测试套件，已通过五个MT系统（神经，基于规则，统计和两个混合MT系统）翻译成英语。比较是根据几个定量参数（错误数量，错误类型，质量等级和自动评估指标得分）进行的。结果表明，CL规则对基于规则，统计和混合MT系统具有积极影响。但是，CL不能改善NMT系统的结果。在应用CL之前和之后，神经系统的输出几乎没有错误，并且在两种情况下，在分析的MT系统中，两种系统的质量最高，显示出应用CL规则后质量下降。对NMT输出的定性讨论阐明了CL导致这种MT结构的问题。

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