首页|Reports Outline Machine Translation Study Results from University of Alacant (Non-fluent Synthetic Target-language Data Improve Neural Machine Translation)

Reports Outline Machine Translation Study Results from University of Alacant (Non-fluent Synthetic Target-language Data Improve Neural Machine Translation)

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Investigators publish new report on Machine Translation. According to news reporting originating from Valencia, Spain, by NewsRx correspondents, research stated, “When the amount of parallel sentences available to train a neural machine translation is scarce, a common practice is to generate new synthetic training samples from them. A number of approaches have been proposed to produce synthetic parallel sentences that are similar to those in the parallel data available.” Financial support for this research came from Ministerio de Ciencia e Innovacin. Our news editors obtained a quote from the research from the University of Alacant, “These approaches work under the assumption that non-fluent target-side synthetic training samples can be harmful and may deteriorate translation performance. Even so, in this paper we demonstrate that synthetic training samples with non-fluent target sentences can improve translation performance if they are used in a multilingual machine translation framework as if they were sentences in another language. We conducted experiments on ten low-resource and four high-resource translation tasks and found out that this simple approach consistently improves translation performance as compared to state-of-the-art methods for generating synthetic training samples similar to those found in corpora.”

ValenciaSpainEuropeEmerging TechnologiesMachine LearningMachine TranslationUniversity of Alacant

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Feb.26)
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