A conference just ended, so it's that time of year! Here are some papers I liked with the usual caveats about recall. Before I go to the list, let me say that I really really enjoyed ACL this year. I was completely on the fence about going, and basically decided to go only because of giving a talk at Repl4NLP, and wanted to attend the business meeting for the discussion of diversity in the ACL community, led by Joakim Nivre with an amazing report that he, Lyn Walker, Yejin Choi and Min-Yen Kan put together. All in all, I'm supremely glad I decided to go: it was probably my favorite conference in recent memory.

As well, e2f's CEO, Michel Lopez, will be speaking along with Lilt's CEO, Spence Green, and GetYourGuide's Anne-Cécile Tomlinson, about our case study for the use of autoadaptive translation technology for large-scale localization projects. Machine Translation (MT) systems are traditionally criticized for poor quality output. Yet combining Machine Translation with auto-adaptive Machine Learning (ML) enables a new paradigm of "machine assistance." Of course, if you really want to learn how these new methods in machine learning, machine translation, and machine assistance are changing the world of translation, feel free to drop us a line!

The marriage of computing power and data is finally bearing fruit in the field of cognitive computing, sometimes called machine learning or, more controversially, artificial intelligence. In its most everyday form, we see it in tools such as Google Translate or Microsoft's Bing Translate, which can translate phrases and documents effortlessly across multiple languages. More futuristically, the promise of self-driving vehicles, which can complete entire road journeys without driver intervention, is already being realised. Yet the biggest revolution in work is happening at some of the most basic levels, such as reading and dissecting legal documents to extract meaning and useful information. The tedious slog of work can be transformed by computers which are able to read and parse legal phrases, and summarise them or enter relevant details into a database or spreadsheet.

If you have a few free minutes, try, for fun, filling them with Google Translate. And you need not be multilingual to enjoy it. Start with something straightforward: Enter an English phrase or sentence (idioms bring particular pleasure). Click a language, say, Spanish, and then "translate." Copy and paste the translated results over your original English phrase, reverse both languages (so that, in this example, Spanish is now where you begin and English is where you end), and again click "translate."

In a globally connected marketplace, new technologies ensure customer transactions won't get lost in translation. The world is becoming increasingly connected, and companies in search of worldwide markets need to be able to communicate with customers in their native tongues. They're depending on sophisticated new machine translation technologies to break down language barriers. "When you first enter a market, the early adopters for any new product -- whether it's a personal care product or a tech product -- tend to be internationally focused and English friendly, so you might think you're doing well," said Ben Sargent, content globalization strategist at the consulting firm Common Sense Advisory. To reach 80 percent of the world's total online population, businesses need to communicate in at least 12 languages, and to reach 98 percent, they need to translate across 48 languages.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages. For eons they have carried out a huge variety of tasks, from manufacturing goods, to transporting people around, to helping us decipher the natural world, to simply entertaining us. Machines can fight, protect, heal, and even teach us. But what they have not been able to do until quite recently is to learn, make decisions, and act on their own. Today, intelligent machines are everywhere. From the Netflix recommendation en- gine to Google Translate to Appleâ s Siri voice-recognition system, artificial intelligence has become sufficiently accurate, reliable, and useful to find its way into numerous devices and applications. These technologies have taken off in parallel with a dramatic expan- sion of the amount and complexity of data, which provides fertile teaching ground from which machines can learn to make intelligent decisions on their own.

As machine learning products continue to target the enterprise, they are diverging into two channels: those that are becoming increasingly meta in order to use machine learning itself to improve machine learning predictive capacity; and those that focus on becoming more granular by addressing specific problems facing specific verticals. And while the latest batch of machine learning products across both these channels may reduce some pain points for data science in the business environment, experts warn that machine learning can't solve two issues regardless of the predictive capacity of the new tools: Last year, new machine learning market entrants focused on speeding up processes around mapping the context that a machine learning algorithm would need to understand in order to predict needs in a given business situation. For example, if a voice translation machine learning product was listening in to a customer service call in order to more quickly help the call operator surface the appropriate solution-based content, the first job of the machine learning product would be to create an ontology that understands the customer call context: things like product codes, industry-specific language, brand items and other niche vocabulary. Products like MindMeld and MonkeyLearn built automatic ontology-creators so the resulting machine learning algorithm had a higher degree of accuracy without the end user first having to enter a whole heap of business-specific data into the product to make it work. Others, like Lingo24, created their own specific vertically-based machine learning engines for industries like banking and IT so that their machine learning translation service could apply the right phrase model to the right situation.

When I attended translation courses, I was assigned to write a commentary on George F. Will's column Reading, Writing and Rationality on the Newsweek issue of March 17, 1986. Even then, with no Internet, and television as the dominant media, students were urged to read. That day, green activists were giving a demonstration of solar energy applications in a public park near the school, and our professor opened his lesson with a witty comment about the experiment he had witnessed during his lunch break. The history of innovation is full of inventors and manufacturers unable to understand the impact and actual use of their own work. Similarly, most innovations do not necessarily use the most recent and sophisticated technology, with their makers showing an outstanding capacity of interpreting and accelerating the transformations that are already underway.

The aim of their collaboration is to achieve machine-based translation between the languages of the European Union so that comprehensible texts are achieved for as many language combinations as possible. Two of the EU-funded research projects are being led by the Saarbrücken computer linguist Josef van Genabith. Anyone who wants to learn Finnish has to be prepared to deal with a complex grammar that includes fifteen different cases. The grammatical cases are marked in part by appending syllables to nouns resulting in a dizzying array of word forms and expressive possibilities. "Teaching a computer to understand all these grammatical nuances and to translate them correctly into another language is exceptionally difficult," says Josef van Genabith, Professor of Translation-Oriented Language Technologies at Saarland University and a Scientific Director at the German Research Center for Artificial Intelligence (DFKI). His team is therefore following a different path.

More than 1.5 billion people use Facebook. And only half speak English. The rest speak so many dozens of other languages, effectively silo'd off from the English speakers and, in many cases, from each other. If you stumble onto a Facebook post in a foreign language, Facebook lets you instantly translate it--in a semi-effective way. And beginning today, millions of people will have the option of instantly translating their own posts into any one of 44 other languages, so that they will automatically show up in your News Feed in your native tongue.