Last fall, Google Translate rolled out a new-and-improved artificial intelligence translation engine that it claimed was, at times, "nearly indistinguishable" from human translation. Jost Zetzsche could only roll his eyes. The German native had been working as a professional translator for 20 years, and he'd heard time and time again that his industry would be threatened by advances in automation. Every time, he'd found, the hype was overblown--and Google Translate's makeover was no exception. It certainly wasn't the key to translation, he thought.

Lexical Simplification is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. This task has wide applicability both as an assistive technology for readers with cognitive impairments or disabilities, such as Dyslexia and Aphasia, and as a pre-processing tool for other Natural Language Processing tasks, such as machine translation and summarisation. The problem is commonly framed as a pipeline of four steps: the identification of complex words, the generation of substitution candidates, the selection of those candidates that fit the context, and the ranking of the selected substitutes according to their simplicity. In this survey we review the literature for each step in this typical Lexical Simplification pipeline and provide a benchmarking of existing approaches for these steps on publicly available datasets. We also provide pointers for datasets and resources available for the task.

There is an intuitive analogy of an organic chemist's understanding of a compound and a language speaker's understanding of a word. Consequently, it is possible to introduce the basic concepts and analyze potential impacts of linguistic analysis to the world of organic chemistry. In this work, we cast the reaction prediction task as a translation problem by introducing a template-free sequence-to-sequence model, trained end-to-end and fully data-driven. We propose a novel way of tokenization, which is arbitrarily extensible with reaction information. With this approach, we demonstrate results superior to the state-of-the-art solution by a significant margin on the top-1 accuracy. Specifically, our approach achieves an accuracy of 80.1% without relying on auxiliary knowledge such as reaction templates. Also, 66.4% accuracy is reached on a larger and noisier dataset.

"Microsoft Monday" is a weekly column that focuses on all things Microsoft. This week, Microsoft Monday includes details about the Windows 10 upgrade program fully ending, the shutdown of Outlook.com When Microsoft originally released Windows 10, it was available as a free upgrade PCs running on Windows 7 and Windows 8.1. After a year, the free upgrade offer was extended for people that were seeking enhanced accessibility features. This workaround will no longer be available after December 31, 2017.

The Google Translate team tests their new app in 27 languages -- you can download it on the Google Play Store (https://goo.gl/translateappandroid) To find out how instant camera magic works, watch this video: https://g.co/go/NLtranslate

We propose a statistical model for natural language that begins by considering language as a monoid, then representing it in complex matrices with a compatible translation invariant probability measure. We interpret the probability measure as arising via the Born rule from a translation invariant matrix product state.

In recent years, MIT scholars have helped develop a whole lexicon of science and math terms for use in Haiti's Kreyòl language. Now a collaboration with Google is making those terms readily available to anyone -- an important step in the expansion of Haitian Kreyòl for education purposes. The new project, centered around the MIT-Haiti Initiative, has been launched as part of an enhancement to the Google Translate program. Now anyone using Google Translate can find an extensive set of Kreyòl terms, including recent coinages, in the science, technology, engineering, and math (STEM) disciplines. "In the past five or six years, we've witnessed quite a paradigm shift in the way people in Haiti talk about and use Kreyòl," says Michel DeGraff, a professor of linguistics at MIT and director of the MIT-Haiti Initiative.

Neural machine translation (NMT) is now mainstream. This was New York University Assistant Professor Kyunghyun Cho's first message during his presentation on NMT at the recent SlatorCon New York on October 12, 2017. When Cho's team started looking into NMT in 2013 and 2014, he said previous MT researchers and industry insiders were convinced it would not work. Efforts in the 1980s and mid-1990s failed, after all. Fast forward to 2017, Cho pointed out that big names like Google, Microsoft, and Facebook use NMT, and sites like Booking.com and even the European Patent Office have all caught the NMT bug.

Facebook has apologised after an error in its machine-translation service saw Israeli police arrest a Palestinian man for posting "good morning" on his social media profile. The man, a construction worker in the West Bank settlement of Beitar Illit, near Jerusalem, posted a picture of himself leaning against a bulldozer with the caption "يصبحهم", or "yusbihuhum", which translates as "good morning". But Facebook's artificial intelligence-powered translation service, which it built after parting ways with Microsoft's Bing translation in 2016, instead translated the word into "hurt them" in English or "attack them" in Hebrew. Police officers arrested the man later that day, according to Israeli newspaper Haaretz, after they were notified of the post. They questioned him for several hours, suspicious he was planning to use the pictured bulldozer in a vehicle attack, before realising their mistake.

Microsoft is delivering the world's first fully neural on device translations in the Microsoft Translator app for Android, customized for the Huawei Mate 10 series. Microsoft achieved this breakthrough by partnering with Huawei to customize Microsoft's new neural technology for Huawei's new NPU (Neural Processing Unit) hardware. This results in dramatically better and faster offline translations as compared to existing offline packs. The Microsoft Translator app with these capabilities comes pre-installed on Huawei Mate 10 devices allowing every Mate 10 user to have native access to online quality level translations even when they are not connected to the Internet. Until now, due to the computational requirements of neural machine translation, it was not possible to do full Neural Machine Translation (NMT) on-device.