The Future of Machine Translation According to

The Future of Machine Translation According to

It used to be the case that when a company or individual wanted to translate content into another language, it required hiring a translator. Investigations into machine translation began in the mid-20th century, but most showed little promise. As it turns out, translation is an incredibly complex process that requires more than just programming in each individual word.

The Current State of Machine Translation

According to, it has come a long way in recent years. Modern companies like Lilt use advanced algorithms and artificial intelligence (AI) to support more advanced machine learning. There are several types of advanced machine translation performed by modern computers.

Rule-Based Machine Translation

Rule-based machine translation (RBMT) was one of the earliest advanced machine translation tools. It allowed linguists to input information not just about direct, word-for-word translations but also each language’s unique semantic, syntactic, and morphological rules. Many of today’s most successful and accurate machine translation services still use advanced forms of RBMT.

Example-Based Machine Translation

Example-based machine translation (EBMT) uses a different framework. Linguists input phrases and parallel texts to make it easier to translate complex language rules. EBMT is generally regarded as a better fit for translating between languages with few semantic and morphological similarities.

Statistical Machine Translation

Statistical machine translation (SMT) analyzes patterns and probability. Advocates of this system in its earliest form pointed to its cost-efficiency and ability to produce reliable translations without the help of linguists as proof of its efficacy. However, even modern SMT systems have trouble with idioms and colloquialisms.

Neural Machine Translation

Neutral machine translation (NMT) was only adopted in 2016. It’s similar in many ways to SMT but makes much greater use of AI and complex algorithms. There’s also a second important Neural: while SMT uses the English language as an intermediary, NMT can translate directly between two non-English languages.

An Even Better Approach

Each of the examples of current machine translation services described above has its benefits and drawbacks. Employing a hybrid approach allows companies to take full advantage of the benefits of each system. Business owners can click here for info about hybrid machine translation.

Looking Toward the Future

There’s no denying that 2020 has brought a lot of changes. Thankfully, those that have occurred in the field of machine translation are heavily skewed toward the positive. The year 2020 is when Unbabel Launches COMET, Blazing New Trail for Ultra-Accurate Machine Translation.

The newest advancement uses open-source, neural machine translation combined with human review. Others in the industry are already beginning to jump on board with this trend. As more translators, linguists, and other professionals engage with new ultra-accurate systems, machine learning will begin to take over and induce even greater leaps forward.

The world may be just as large as ever, but today’s business owners and consumers have a far greater reach when it comes to communication. Unless everyone wakes up one day and decides to adopt a universal language standard, the translation process will always be a part of international communication. This is the future, so business owners would do well to embrace it now.

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