Neural machine translation (NMT), machine learning and (Big) data are becoming evergreen trending topics within the translation & localisation industry. We ask many questions: How does NMT work? Is it really going to revolutionize our industry? Are machines going to put us all out of our jobs? What’s the real value of (language) data for the translation industry? Should I share my data or keep them to myself? There isn’t one single answer, but here are links to some #SmartReads on these topics.
In the last few weeks, Slator has published few articles on NMT and artificial intelligence. If you don’t have any clear idea of what NMT is all about – most importantly, is it a hype or not? – you might want to start with the presentation on neural machine translation given by John Tinsley (Iconic) during the SlatorCon event of May 9, 2017. The presentation can be downloaded for free, but you do have to register.
Two more articles worth mentioning available on the Slator’s website are: Neural machine translation research accelerates dramatically and Facebook to open its neural machine translation. The first is an overview of the academic research on NMT while the second looks at the work that Facebook is doing to make “universal translation a reality”.
Kirti Vashee’s blog, EMpTY Pages, also offers interesting points of view about NMT. His articles are slightly more technical than the ones published by Slator, but still accessible. Here are some suggestions:
- Specializing Neural Machine Translation in SYSTRAN – On the challenges of building a customised NMT system.
- The problem with BLEU and Neural Machine Translation – On why the BLEU score might be misleading in comparing different MT systems.
- Optimising LSP performance in the Artificial Intelligence Landscape – This is a more promotional guest post (but still useful) written by Danny de Wit about Tolq, a platform that “provides end-to-end human translation solutions for high-volume enterprise clients around the world“, to quote Tolq’s website. In this article, De Wit states:
Within a period of just a few years, the entire translation industry will be reshaped. Translation is not the only industry where the impact will be felt. The same will go for virtually any industry. The technology is that powerful. But it won’t be in the shape of zero-shot NMT.
Artificial intelligence is a wave of innovation that you have to jump on. And do so today.
Fortunately, it turns out that one key thing to help you do this is one that LSP’s have been doing already for years: collecting high-quality data.
The New Data Economy
Let’s move on to data, then. In the first week of May, The Economist published an entire issue dedicated to data big and small.
Two articles stood out. The first, Data is giving rise to a new economy, discusses the new data-based economy and its implications:
Data are to this century what oil was to the last one: a driver of growth and change. Flows of data have created new infrastructure, new businesses, new monopolies, new politics and—crucially—new economics. Digital information is unlike any previous resource; it is extracted, refined, valued, bought and sold in different ways. It changes the rules for markets and it demands new approaches from regulators. Many a battle will be fought over who should own, and benefit from, data.
The second article, The world’s most valuable resource is no longer oil, but data, examines the necessity of rebooting the antitrust laws for the new data economy:
Smartphones and the internet have made data abundant, ubiquitous and far more valuable. Whether you are going for a run, watching TV or even just sitting in traffic, virtually every activity creates a digital trace—more raw material for the data distilleries. As devices from watches to cars connect to the internet, the volume is increasing: some estimate that a self-driving car will generate 100 gigabytes per second. Meanwhile, artificial-intelligence (AI) techniques such as machine learning extract more value from data. Algorithms can predict when a customer is ready to buy, a jet-engine needs servicing or a person is at risk of a disease. Industrial giants such as GE and Siemens now sell themselves as data firms.
For another interesting point of view on this topic, you might want to add to your reading list an article by Evgeny Morozov on data protection, published back in December 2016 on the Guardian’s website. Finally, wrap up your data session with a talk by Jaron Lanier on the digital economy (from 2015):
A primary problem, according to Lanier, is that “we’ve created only half of the economy.” While platforms and services such as Uber and Facebook provide what Lanier calls “informal benefits,” such as convenience, they have not created wealth for the majority of people using them. Instead, wealth, and more importantly, power, information, and data, have become concentrated in large companies, while ordinary users find themselves in increasingly precarious work arrangements, with decreasing privacy and control.
Machine Learning and The Future of the Translation Industry
An article by Jaap van der Meer (TAUS), titled The Story of The Translation Industry in ’22, can give you an idea of what technology could bring us.
The translation companies of today will not be the same in 2022. We’ll see a split in translation tech and the creative networks, the data factories and the storytelling, the platforms and the boutiques, perhaps sometimes still operating under the same umbrella, but clearly separated in functions. Sounds familiar, this story? Perhaps you are thinking about the paradigm shift in the advertising and marketing industry. Once thought to be so creative, it had its own unique place in an environment of factory and office automation. But now, after a few decades of data storms, the business of the prestigious advertising agencies has changed, fundamentally.
For more on machine learning, robots and how we will be working in the future, try these articles:
Ready or Not, The Future is Now – An article by journalist Sarah Fister Gale on the implication of machine learning in the workplace. For some thoughts on the combination of language and technology, you might want to take a look at Will robots destroy human language? ; and on the possibility that future technology might eliminate the need for language learning, here is an article published by Salon, Could the language barrier actually fall within the next 10 years?