In January, Google announced a cool new feature that turns the Google Assistant into a two-way language interpreter, but it only worked visually on smart displays, which generally aren't used in the real world, when people are traveling. But now, just in time for the holidays, Google is finally making Interpreter Mode available on mobile Android and iOS phones. As always, Google is rolling the feature out and it could take up to a week for it to make it across the network. You start by asking Google to "be my Spanish translator," and then the Assistant takes over. You speak your phrase, and Google translates it, in audio and text and in real time, and the person on the other end can speak into your phone with the answer and keep the two-way conversation going.

Word translation is a problem in machine translation that seeks to build models that recover word level correspondence between languages. Recent approaches to this problem have shown that word translation models can learned with very small seeding dictionaries, and even without any starting supervision. In this paper we propose a method to jointly find translations between a pair of languages. Not only does our method learn translations in both directions but it improves accuracy of those translations over past methods.

Many of us are familiar with Google Translate, translation applications for travellers' smartphones and the instruction manuals of various devices and products. Professional translators also make use of machines. Training a computer to translate between two specific languages takes millions of sentences or billions of words worth of text. Maarit Koponen, a postdoctoral researcher at the University of Helsinki, is investigating which errors made by machines lead to misunderstandings and how those mistakes could be identified. The learning algorithms behind machine translation are called artificial intelligence, but machines are not intelligent in the way humans or the super AIs of science-fiction films are.

As a special machine translation task, dialect translation has two main characteristics: 1) lack of parallel training corpus; and 2) possessing similar grammar between two sides of the translation. In this paper, we investigate how to exploit the commonality and diversity between dialects thus to build unsupervised translation models merely accessing to monolingual data. Specifically, we leverage pivot-private embedding, layer coordination, as well as parameter sharing to sufficiently model commonality and diversity among source and target, ranging from lexical, through syntactic, to semantic levels. In order to examine the effectiveness of the proposed models, we collect 20 million monolingual corpus for each of Mandarin and Cantonese, which are official language and the most widely used dialect in China. Experimental results reveal that our methods outperform rule-based simplified and traditional Chinese conversion and conventional unsupervised translation models over 12 BLEU scores.

Manipulating training data leads to robust neural models for MT.

American localization specialist Lionbridge Technologies has been employing machine translation tools for many years. Eventually, its customers started asking for multilingual training data. Today, Lionbridge has a separate division entirely dedicated to AI, doing everything from collection of chatbot training data to image annotation, audio transcription and even multilingual content moderation services. To find out more about the work of the division, AI Business talked to Aristotelis Kostopoulos, vice president of product solutions, artificial intelligence at Lionbridge. Q: The AI division at Lionbridge grew out of the machine translation business, but today it does so much more.

At a height of 100m up the amazing A'DAM Tower in central Amsterdam, the altitude wasn't a problem at Global Transformation Summit (GTS) but keeping up with the many shared experiences and fast exchange of ideas was! GTS Amsterdam brought together global brands, connecting international business leaders and senior marketing and localization professionals. What was the common ground? Many insights shared and new contacts made. As content types and volumes continue to increase – the growth of content on the internet doubles every 18 months – brands need to converge content, collaborate internally, and ensure the customer experience is consistent and personal, to stand out from online competition. This means re-imagining how we work – looking to define how multilingual content performs beyond traditional KPIs.

OpenSeq2Seq main goal is to allow researchers to most effectively explore various sequence-to-sequence models. The efficiency is achieved by fully supporting distributed and mixed-precision training. OpenSeq2Seq is built using TensorFlow and provides all the necessary building blocks for training encoder-decoder models for neural machine translation, automatic speech recognition, speech synthesis, and language modeling. Speech-to-text workflow uses some parts of Mozilla DeepSpeech project. Beam search decoder with language model re-scoring implementation (in decoders) is based on Baidu DeepSpeech.

In recent years, natural language processing (NLP) has become a part of our everyday lives. Smartphones now come equipped with NLP-powered voice assistants that interpret and understand human speech in order to provide relevant responses to user queries. NLP also helps translation apps break down communication barriers by analyzing input in one language and transforming it into another language. Even word processors rely on NLP to check the grammar, logic, and syntax of written input. And NLP is now an integral part of customer service; it's used to guide people to the right representative through verbal commands. Yet, few people actually understand how NLP plays a role in making them possible.

Is it a Good Idea to Trust Machine Translations? Although machine translation (MT) has become extremely popular in recent years, it still has a long way to go before it can substitute a human translator. Does it mean that you shouldn t use machine translation?