Machine translation: a critical look at the performance of rule-based and statistical machine translation

Banitz, Brita

doi:10.5007/2175-7968.2020v40n1p54

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…However, the basis of the analysis in the present paper was simply the occurrence frequencies of accident-related terms, and thus errors related to sentence structure, grammar, framing, and sentiment mapping would not affect the results. Furthermore, various studies on Google Translate's performance report high quality scores overall [78,79], particularly for translations from the German language [80], even when translation errors other than the errors for term translations are included. In conclusion, the possible effects of machine translation errors can be considered minimal in the present study.…”

Section: Data Collection and Data Cleaningmentioning

confidence: 99%

Analyzing a Decade of Wind Turbine Accident News with Topic Modeling

Ertek

Kailas

2021

Sustainability

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Despite the significance and growth of wind energy as a major source of renewable energy, research on the risks of wind turbines in the form of accidents and failures has attracted limited attention. Research that applies data analytics methodologically in this context is scarce. The research presented here, upon construction of a text corpus of 721 selected wind turbine accident and failure news reports, develops and applies a custom-developed data analytics framework that integrates tabular analysis, visualization, text mining, and machine learning. Topic modeling was applied for the first time to identify and classify recurring themes in wind turbine accident news, and association mining was applied to identify contextual terms associated with death and injury. The tabular and visual analyses relate accidents to location (offshore vs. onshore), wind turbine life cycle phases (transportation, construction, operation, and maintenance), and the incidence of death and injury. As one of the insights, more incidents were found to occur during operation and transportation. Through topic modeling, topics associated most with deaths and injuries were revealed. The results could benefit wind turbine manufacturers, service providers, energy companies, insurance companies, government bodies, non-profit organizations, researchers, and other stakeholders in the wind energy sector.

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Section: Data Collection and Data Cleaningmentioning

confidence: 99%

Analyzing a Decade of Wind Turbine Accident News with Topic Modeling

Ertek

Kailas

2021

Sustainability

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“…Testing MT engines has been a very fertile research area for some years, and there are some relevant studies in Human assessment and post-edition which relate to translators work. Brita Banitz (2020), for instance, used Human assessment along with TER score measure to evaluate rule-based and statistical machine translation and explained in an essay their performance with English-German phrases taken from Mark Twain's, The Awful German Language.…”

Section: Introductionmentioning

confidence: 99%

Challenging machine translation engines: some Spanish-English linguistic problems put to the test

Aguilar

2023

Cad. Trad.

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This work is an evaluation of machine translation engines completed in 2018 and 2021, inspired by Isabelle, Cherry & Foster (2017), and Isabelle & Kuhn (2018). The challenge consisted of testing MTs Google Translate and Bing and DeepL in the translation of certain linguistic problems normally found when translating from Spanish into English. The divergences representing a “challenge” to the engines were of morphological and lexical-syntactical types. The absolute winner of the challenge was DeepL, in second place was Bing from Microsoft, and Google was the engine that was the poorest in the management of the linguistic problems. In terms of time, when comparing the engines three years apart, it was found that DeepL was the only one that enhanced its performance by correcting a problem it had before in a test sentence. This was not the case for the other two, on the contrary, their translations were of lower quality. These machines do not seem to be consistent in the manner in which they are improved. These findings may be valuable for translators who may work with these systems as pre or post-editors so that their efforts may be better directed.

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