Exploring how machine translation can revolutionize the way of translating information?

Machine translation refers to the process of translating vast amounts of information involving millions of words from one neural language to another. Translation companies use this translation technique to strengthen the productivity of their translators, cut costs, and provide post-editing services to customers. All the machine translation products, until 2016, were based on algorithms that use statistical methods to establish the best possible translation to a given word. The technology, called Statistical Machine Translation, comprises advanced statistical analysis to foresee the best possible translations for a word given the context of a few surrounding words.

Here is a look at top machine learning trends everyone should consider in 2020.

Automated translation

Translations are built on vast dictionaries and sophisticated linguistic rules. This improves users’ out-of-the-box translation quality by adding their terminology to the translation process. With advanced technologies embedded in translation systems automating translation memory, translators can focus on the tasks that maximize quality. As automated translation technologies will continue to get better and smarter, they can assist in early-stage and not-for-publication tasks.

Faster Translation Delivery

Machine translation provides faster translation delivery and enables reduced time-to-market translation approach. It is becoming a striking technology and has come far from those early stages. In today’s translation technologies, modern engines are capable of competing with human translators, to a degree. Machine translation can provide translated content in just a matter of seconds depending on how much content needs to be translated. This translation technology along with human translators has begun integrating naturally and seamlessly, paving the way for new innovative localization workflows for globally-minded companies.

Rule-Based Machine Translation

Rule-based machine translation provides good out-of-domain quality. This translation system is based on linguistic information about the source and target languages retrieved from unilingual, bilingual or multilingual dictionaries and grammars covering the main semantic, morphological, and syntactic regularities of each language respectively. The first ideas of rule-based machine translation appeared in the 70s, where the scientists peered over the interpreters’ work, trying to compel the vast sluggish computers to repeat those actions.

Smaller High-Quality Models

There were many attempts in last year to train large and very deep transformer-based models with billions of parameters. Those massive models very often turned out to be enormously over-parameterized and can often do a much better job when they are compressed in size. Most consider knowledge distillation strategies to explore the trade-off between the model size and quality. The idea here is to first develop a large teacher model and then train and deploy a smaller student model, which would impersonate the teacher’s behavior.

Multi-Domain Neural Machine Translation

Neural machine translation (NMT) enables the use of the neural network type models to train a statistical model for machine translation. In this translation method, a single system can be trained directly on the source and target text to translate, unlike statistical MT that requires the pipeline of systems in between. Multilingual NMT has pivoted its way into different settings from different aspects, bringing numerous benefits to low-resources languages, and can be especially effective for similar languages.