This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/) 2 R. Al-Sabbagh / Data in Brief 54 (2024) 1 10271 Subject Computer Science, Social Sciences Specific subject area Natural Language Processing, machine translation, large-language models, translation studies, cross-linguistic analysis, lexical semantics Data format Translated and aligned Type of data Texts (Bilingual tables in Microsoft Excel files) Data collection The ArzEn-MultiGenre dataset consists of three genres: song lyrics, novels, and subtitles. The data was gathered from various sources using different methods. A website was crawled for song lyrics using an in-house web crawler, and professional translators manually translated the lyrics into English. For novels, hard copies were collected in English and Egyptian Arabic, then scanned and converted into text files using an Optical Character Recognizer (OCR). The OCR output was then manually reviewed and aligned.

In this article, we'll take a closer look at Translation Hub's powerful features, and the ways it is helping customers do more with their content. Translation Hub is a fully-managed, self-serve translation offering, powered by Google AI and built for the enterprise. With Translation Hub, businesses can instantaneously translate content into 135 languages with a single click, via an intuitive interface that integrates human reviews (i.e., a "human in the loop") where required. Organizations need to be able to share the output of AI-powered translation with localization teams or agencies, for review. They need to save time by leveraging glossaries or customer machine learning (ML) models.

At Next '18 this past July, we announced a range of updates to our AI and machine learning offerings aimed at making AI more accessible to developers. With the excitement of Next behind us, we thought we'd share a little more on these updates and how they can help you quickly and easily inject AI into your applications. Cloud AutoML is a suite of machine learning products that leverages Google's state-of-the-art transfer learning and neural architecture search (NAS) technology so you can easily train high quality custom models, even if you have limited experience with machine learning. This delivers the best of both worlds: high model quality and ease of use. This new suite of products aligns with our mission to democratize AI, and make it easy, fast and useful for all developers and enterprises.

AI has evolved dramatically in the last two decades. Technologies like image recognition and machine translation are now a part of everyday life for millions. AI has transformed industries all over the world, and created entirely new ones. And in the process, it promises an increase in quality of life and work never before imagined. But there's still much more we can do--after all, AI is still a nascent field of many opportunities and challenges.

Artificial intelligence has become a sort of secret weapon in the battle to build the best cloud service platform. Google Cloud Platform is currently the underdog, trailing both Amazon Web Services and Microsoft Azure. But Google is betting robust AI will give it the edge it needs to catch up. At the annual Google Cloud Next conference which kicked off July 24 in San Francisco the company unveiled a series of AI-based product releases and enhancements for its analytics and machine learning tools, additional applications on G Suite, and new IoT products. Earlier this week, Google parent company Alphabet reported its Q2 earnings, which were ahead of Wall Street's expectations.

Pre-trained machine learning models are good enough for many use cases, but to get the most out of this technology, you need custom models. Given that it's not exactly easy to get started with machine learning, Google (and others) have opted for a hybrid approach that allows users to upload their own data to customize the existing models. Google's version of this is AutoML, which until now only provided this capability for machine vision tasks under the AutoML Vision moniker. Starting today, the company is adding two new capabilities to AutoML: AutoML Natural Language for predicting text categories and AutoML Translation, which allows users to upload their own language pairs to achieve better translations for texts in highly specialized fields, for example. In addition, Google is launching AutoML Vision out of preview and into its private beta.

The cloud is disrupting traditional operating models for IT departments and entire organizations. At the Google Cloud Next conference in San Francisco Tuesday, Google laid out how it's bringing artificial intelligence to developers, as well as integrating more AI capabilities throughout its cloud products. Artificial intelligence has long been a cornerstone of Google Cloud's value proposition, but to win more customers it needs to make those capabilities more accessible. It also has to contend with Amazon Web Services and the fast-growing Microsoft Azure, which have been building up their own AI-powered offerings and creating their own plans for lowering the barrier to entry. During the Day One Next keynote, Google Cloud CEO Diane Greene noted that Google is heavily investing in two key areas: AI and security.