Machine Translation Human Evaluation

In Proceedings of the International Workshop on Spoken Language Translation Bruges Belgium pp. While automatic measures are an invaluable tool for the day-to-day development of machine translation sys-tems they are an imperfect substitute for human assessment of translation quality.

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We propose a method of automatic ma-chine translation evaluation that is quick inexpensive and language-independent that correlates highly with human evalu-.

Machine translation human evaluation. Human evaluation of machine translation quality is still very important even though there is no clear consensus on the best method. In some cases the quality of system output is measured directly such as with human judgments. Integration of human and machine translation is a promising workflow for the future.

Therefore these algorithms can help people communicate in different languages. Machine translation human evaluation. Human translation still provides the best translation quality but it is in general time-consuming and expensive.

But past research has shown that other issues arise when crowd workers without a background in translation are hired as a cost-saving measure. The various steps in model estimation word alignment phrase extraction feature weight optimization etc are con- ducted sequentially resulting in a translation system with a static set of models and feature weights. Machine translation will not replace human translation but it can serve as a tool to increase.

This iteration of MT relies on AI and generates translations through neutral networks which according to IBM are a subset of machine learning that is designed to mimic the human he way. 2018 a breakthrough result where we showed for the first time a Machine Translation system that could perform as well as human translators in a specific scenario Chinese-English news translation. Even professional translators can disagree on whether fluency trumps accuracy in a given sentence.

Machine translation is traditionally treated as a batch learning and prediction task. Human evaluations of machine translation are extensive but expensive. Such algorithms are used in common applications from Google Translate to apps on your mobile device.

In a typical scenario human judges evalu-ateasystemsoutputor hypothesisbycomparing it to a source sentence andor to a reference trans-lation. It is a key element in the development of machine translation systems as automatic metrics are validated through correlation with human judgment. 3 Human evaluation Weevaluatedthesharedtasksubmissionsusingboth manual evaluation and automatic metrics.

Traditional and recently proposed metrics for automatic machine translation evaluation are described. The most recent development in the decades-long evolution of machine translation came in 2014 with the advent of Neural Machine Translation NMT. The evaluation of machine translation MT is of crucial im-portance and has a long research history.

Both human and automatic evaluation have been explored extensively within the MT community in the effort to ﬁnd more and more suit-able efﬁcient and reliable methods and metrics. Human eval-uations can take months to ﬁnish and in-volve human labor that can not be reused. This was an exciting breakthrough in Machine Translation research but the system we built for this project was a complex heavyweight.

In other cases it is measured by performing reading tests or other downstream tasks with the system output and in still. Then they score the hypothesis according. Human evaluation a favorite topic of MT-focused academic research is not necessarily a silver bullet.

To evaluate the quality of Machine Translation MT systems to determine which algorithms and techniques are to be considered the new state-of-the-art. 62 69Google Scholar. Human evaluation on the other hand is time-consuming and expensive as well as subjective.

Several methods of evaluation using human judgments are frequently employed in the machine translation community. In March 2018 we announced Hassan et al. Manual evalua-tion is time consuming and expensive to perform.

An investigation of evaluation based on Post-editing and its relation with Direct Assessment. We suggest that human evaluation procedure could be improved in order to achieve better efficiency and objectivity by developing a quantitative metric based on quality parameters and standards used in translation industry to evaluate human translation. Machine translation is the task of translating from one natural language to another natural language.

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