Remove en resources
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Much of the Web Is Machine Translated: Insights from Multi-Way Parallelism

Hacker News

Multi-way parallel, machine generated content not only dominates the translations in lower resource languages; it also constitutes a large fraction of the total web content in those languages.

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Build a multilingual automatic translation pipeline with Amazon Translate Active Custom Translation

AWS Machine Learning Blog

The following example is extracted from D2L-en book and D2L-zh book. We have tested a set of documents in four pipelines: English into Chinese (en to zh), Chinese into English (zh to en), English into Spanish (en to es), and Spanish into English (es to en).

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Translate documents in real time with Amazon Translate

AWS Machine Learning Blog

en es source-lang.txt Conclusion The real-time document translation feature in Amazon Translate can expedite time to market by enabling easy integration with content creation and localization. For more information about Amazon Translate, visit Amazon Translate resources to find video resources and blog posts, and refer to AWS Translate FAQs.

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Universal Speech Model (USM): State-of-the-art speech AI for 100+ languages

Google Research AI blog

for closed captions), can perform automatic speech recognition (ASR) not only on widely-spoken languages like English and Mandarin, but also on under-resourced languages like Amharic, Cebuano, Assamese, and Azerbaijani to name a few. For en-US, USM has a 6% relative lower WER compared to the current internal state-of-the-art model.

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Amazon Translate enhances its custom terminology to improve translation accuracy and fluency

AWS Machine Learning Blog

en fr es tutor éducateur tutor sheep agneau oveja walking promenant para caminar burger sandwich hamburguesa action-specific spécifique à l’action especifico de acción order commande commande Exploring the custom terminology feature Let’s translate the sentence “she was a great tutor” with Amazon Translate.

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Manage your Amazon Lex bot via AWS CloudFormation templates

AWS Machine Learning Blog

Managing your Amazon Lex bots using AWS CloudFormation allows you to create templates defining the bot and all the AWS resources it depends on. AWS CloudFormation provides and configures those resources on your behalf, removing the risk of human error when deploying bots to new environments. Resources: # 1.

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Serve Watson NLP Models Using Knative Serving

IBM Data Science in Practice

oc apply -f -<<EOF apiVersion: serving.knative.dev/v1 kind: Service metadata: name: watson-nlp-kn spec: template: metadata: annotations: queue.sidecar.serving.knative.dev/resourcePercentage: "10" spec: initContainers: - name: ensemble-workflow-lang-en-tone-stock image: cp.icr.io/cp/ai/watson-nlp_classification_ensemble-workflow_lang_en_tone-stock:1.0.6