San Francisco: To improve the quality of translations on its platform, Facebook has infused artificial intelligence (AI) into its translation services that account for more than 2,000 translation directions and 4.5 billion translations each day. With over two billion users, Facebook supports over 45 languages.
“We recently switched from using phrase-based machine translation models to neural networks to power all of our back-end translation systems,” the company said in a blog post.
“These new models provide more accurate and fluent translations, improving people’s experience consuming Facebook content that is not written in their preferred language,” it added.
According to Facebook, their previous phrase-based statistical techniques were useful but they also had limitations.
“One of the main drawbacks of phrase-based systems is that they break down sentences into individual words or phrases, and, thus, when producing translations, they can consider only several words at a time,” the researchers said.
This leads to difficulty translating between languages with markedly different word orderings.
To remedy this and build our neural network systems, Facebook started with a type of recurrent neural network known as sequence-to-sequence LSTM (long short-term memory) with attention.
Such a network can take into account the entire context of the source sentence and everything generated so far, to create more accurate and fluent translations.
The Facebook AI Research (FAIR) team recently published research on using convolutional neural networks (CNNs) for machine translation.