A Shocking Amount of the Web is Machine Translated!
So essentially,
Most of the web is reletively low quality machine translated content
Paper: A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism (11 pages)
Researchers from AWS AI Labs, UC Santa Barbara, and Amazon Alexa are interested in understanding the trends of machine-translated content ie content on the internet that is automatically translated into different languages.
Hmm..What’s the background?
A significant amount of web content is generated by users with limited language proficiency, leading to lower-quality English content being mass-translated into lower-resource languages. Here are some effects of low-quality MT content on large language models:
Reduced accuracy: Low-quality translations decrease performance, affecting user experience.
Limited applicability: Low-quality MT content may limit model applicability, particularly in specific domains or high-stakes applications.
Negative impact on lower-resource languages: Overrepresentation of low-quality content in these languages can widen the digital divide.
Ok, So what is analysis in the research paper?
Web content translation: The paper analyzes the extent to which web content is translated into multiple languages using machine translation, and includes an examination of the type of content being translated, revealing a selection bias where low-quality English content is mass-translated into lower-resource languages, potentially impacting the quality and consistency of translations in these languages.
Multi-way parallel translations: The paper analyzes the characteristics of multi-way parallel translations, comparing the translations of the same content across multiple language pairs and machine translation systems. This analysis helps assess the quality and consistency of machine translations, and it can inform the development of better MT algorithms and techniques.
And what’s next?
The paper's findings emphasize the need for further research in this area to better understand the implications and develop strategies to address the challenges associated with low-quality machine-translated content by setting data quality standards and exploring alternative data sources and preprocessing techniques, the community can ensure the continued improvement and reliability of machine translation algorithms and large language models.
So essentially,
Most of the web is reletively low quality machine translated content