Forget phrase books or even Google Translate. New translation devices are getting closer to replicating the fantasy of the Babel fish, which in the "Hitchhiker's Guide to the Galaxy" sits in one's ear and instantly translates any foreign language into the user's own. The WT2 Plus Ear to Ear AI Translator Earbuds from Timekettle are already available, while the over-the-ear "Ambassador" from Wavery Labs is scheduled for release this year. Both brands are wireless, and come with two earpieces that must be synced to a single smartphone connected to Wi-Fi or cellular data. These devices "bring us a bit closer to being able to travel to places in the world where people speak different languages and communicate smoothly with those who are living there," said Graham Neubig, an assistant professor at the Language Technologies Institute of Carnegie Mellon University and an expert in machine learning and natural language processing.

Amazon Translate, Amazon Web Service's real-time translation service, is getting an update with support for 22 new languages. The announcement comes a week ahead of the AWS re:Invent conference, where AWS will promote Translate and a slew of other AI-powered tools for its cloud customers. AWS on Monday also announced new services related to image recognition, voice-based UIs and IOT. What is AI? Everything you need to know about Artificial Intelligence Amazon Translate now supports a total of 54 languages and dialects, with 2,804 language pairs now supported. The neural machine translation service enables customers to easily translate information from one language to many.

Samsung Electronics' Global Research & Development (R&D) Centers play a key part in developing artificial intelligence (AI) capabilities for real-world usage. A credit to the work this advanced R&D branch of Samsung undertakes, both Samsung R&D Institute Poland and Samsung Research America AI Center have recently won two prestigious global challenges. This year, Samsung R&D Institute Poland won first place in two categories, the first being text-to-text translation from English to Czech and the second – an end-to-end system translating English speech into German text. For the text-to-text translation category, researchers worked to develop a model to translate the transcript of a spoken English-language TED Talk into Czech. Developing their winning model required the Samsung team to develop large, filtered corpora from which to work and generate as much synthetic data as possible.

We present novel automatic metrics for machine translation evaluation that use discourse structure and convolution kernels to compare the discourse tree of an automatic translation with that of the human reference. We experiment with five transformations and augmentations of a base discourse tree representation based on the rhetorical structure theory, and we combine the kernel scores for each of them into a single score. Finally, we add other metrics from the ASIYA MT evaluation toolkit, and we tune the weights of the combination on actual human judgments. Experiments on the WMT12 and WMT13 metrics shared task datasets show correlation with human judgments that outperforms what the best systems that participated in these years achieved, both at the segment and at the system level.

Just a few weeks ago, I told you about 7 new languages supported by Amazon Translate, our fully managed service for machine translation. Well, here I am again, announcing no less than 22 new languages and variants, as well as 6 additional AWS Regions where Translate is now available. Introducing 22 New Languages And Variants That's what I call an update! In addition to existing languages, Translate now supports: Afrikaans, Albanian, Amharic, Azerbaijani, Bengali, Bosnian, Bulgarian, Croatian, Dari, Estonian, Canadian French, Georgian, Hausa, Latvian, Pashto, Serbian, Slovak, Slovenian, Somali, Swahili, Tagalog, and Tamil. Congratulations if you can name all countries and regions of origin: I couldn't!

We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provide predictions on what pronoun class label should replace a placeholder value in the target-language text, provided in lemma-tised and PoS-tagged form. We provided four subtasks, for the English-French and English-German language pairs, in both directions. Eleven teams participated in the shared task; nine for the English-French subtask, five for French-English, nine for English-German, and six for German-English. Most of the submissions outperformed two strong language-model- based baseline systems, with systems using deep recurrent neural networks outperforming those using other architectures for most language pairs.

Large mobility datasets collected from various sources have allowed us to observe, analyze, predict and solve a wide range of important urban challenges. In particular, studies have generated place representations (or embeddings) from mobility patterns in a similar manner to word embeddings to better understand the functionality of different places within a city. However, studies have been limited to generating such representations of cities in an individual manner and has lacked an inter-city perspective, which has made it difficult to transfer the insights gained from the place representations across different cities. In this study, we attempt to bridge this research gap by treating \textit{cities} and \textit{languages} analogously. We apply methods developed for unsupervised machine language translation tasks to translate place representations across different cities. Real world mobility data collected from mobile phone users in 2 cities in Japan are used to test our place representation translation methods. Translated place representations are validated using landuse data, and results show that our methods were able to accurately translate place representations from one city to another.

Early December marks the kickoff of Amazon's AWS re:Invent conference in Las Vegas, and ahead of the festivities the tech giant has unveiled a slew of product enhancements. To this end, Amazon Translate, the company's cloud machine translation service that delivers language translation via API requests, today gained new languages and variants and expanded to new regions globally. By way of a refresher, Translate -- which debuted in preview in November 2017 ahead of general availability last April -- taps AI that aims to deliver more accurate and natural-sounding translation than statistical or rule-based approaches. It allows customers to define how brand names, character names, model names, and other unique terms get translated. When used in tandem with a natural language processing app, Translate also facilitates sentiment analysis.

In 2018 we saw a number of landmark research breakthroughs in the field of natural language processing (NLP). The introduction of transfer learning and pretrained language models in NLP pushed forward the limits of language understanding and generation. These also dominated NLP progress this year. Teams from top research institutions and tech companies explored ways to make state-of-the-art language models even more sophisticated. Many improvements were driven by massive boosts in computing capacities, but many research groups also discovered ingenious ways to lighten models while maintaining high performance. In this article, we summarize 11 research papers covering key language models presented during the year as well as recent research breakthroughs in machine translation, sentiment analysis, dialogue systems, and abstractive summarization.

Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc non systematic engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool ( ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated and real data sets and discuss possible difficulties in achieving satisfactory predictive power. 2 Introduction Metabolic engineering 1 enables us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels 2,3 or anticancer drugs.