Understanding foreign languages has always been a barrier for individuals and businesses seeking to expand in other countries. Language learning and translation has somewhat aided this and with the recent technological advancements in artificial neural networks this may become even easier.
In simple terms, artificial neural networks normally shortened to "neural networks" are a type of artificial intelligence (AI) that mimics the biological neural networks seen in animals.
Erol Gelenbe, a professor at Imperial College London's department of electrical and electronic engineering, is one of the leading researchers in the field, whose interest in artificial neural networks (here on shortened to "neural networks") developed from his earlier work on anatomy. He started by trying to build mathematical models of parts of human and animal brains, then graduated to using neural networks to route data traffic across the internet and other large networks.
Gelenbe says translation has three different aspects, whether carried out by a machine or a human being. The first is word to word translations, which can be accelerated or simplified using neural networks and other fast algorithms. The second is mapping the syntax, which means the neural network will have to "understand" the nuances of grammar in both languages. The third is using context to translate, which is extremely important as it directly affects which words are chosen.
Gelenbe uses English and German as an example: "Neural networks can be used for each of these steps as a way to store and match patterns, for example matching school' with schule', matching to' with nach', or learning and matching the grammatical structures".
A robotic rosetta stone?
Google and Microsoft both introduced neural machine translation back in November 2016. It differs from the previous large-scale statistical machine translation, as it translates whole sentences at a time instead of just one or two words at a time. In a blog post, Google explained how the sentence is translated in its broader context and is then rearranged and adjusted "to be more like a human speaking with proper grammar". This makes it easier to translate larger bodies of text as they are taken sentence by sentence, so paragraphs and articles will be translated with fewer errors or instances of miscomprehension. Microsoft has a useful tool to highlight the difference between neural networks and statistical machine translation, which shows how neural translation sounds much more natural. And the best part? Over time neural networks learn to create better and more natural translation.
But neural network-powered translation isn't all about completely new innovations - it also builds technologies being used in other domains, such as LSTM.
LSTMs support machine learning and can learn from experience, depending on how they are applied. Since 2015, Google's speech recognition on smartphones has been based on self-learning long short-term memory (LSTM) recurrent neural networks (RNNs) and the technology has been extended to other products, including Google Translate.
Jrgen Schmidhuber, professor and co-director of the Swiss Dalle Molle Institute for Artificial Intelligence and president of NNAISENSE, who developed LSTM-RNN technology, predicts that in the future these systems will enable "end-to-end video-based speech recognition and translation including lip-reading and face animation".
"For example, suppose you are in a video chat with your colleague in China. You speak English, he speaks Chinese. But to him it will seem as if you speak Chinese, because your intonation and the lip movements in the video will be automatically adjusted such that you not only sound like someone who speaks Chinese, but also look like it. And vice versa," Schmidhuber explains.
