The translation industry is constantly evolving to meet the needs of clients, and neural machine translation (NMT) is the latest step in that progression. With its ability to translate entire sentences at once, NMT’s output can be similar to that of a human translation. As more businesses become global, NMT could have a huge impact on the translation industry.
What is Neural Machine Translation?
NMT uses neural network-based technology to achieve more contextually precise translations, rather than broken sentences that are translated one word at a time. Using a large artificial neural network to calculate the probability of a words sequence, NMT places complete sentences into one integrated model.
Intended to mimic the neurons of a human brain, NMT’s neurons can learn and gather information, make connections, and evaluate input as an entire unit. NMT conducts its analysis in two phases: encoding and decoding. During the encoding stage, text from the source language is entered into the machine and subsequently sorted into language vectors. Words that are similar in context will be placed in comparable word vectors. Next, the decoding phase efficiently and seamlessly transmits the vectors into the target language. Throughout the translation process, the technology is not simply translating words and phrases; instead, it is translating context and information.
History of Machine Translation
Ideas about machine translation can be followed all the way back to the seventeenth century, but it was in the 1950s when research that was funded by the U.S. government generated international interest in machine translation. In the 1970s, rule based machine translation came on the scene to create grammatical rule sets for source and target languages. Next, statistical machine translation (SMT) was developed, creating models by studying source language and target language alignments, then using them to create a translation.
As the translation industry moves forward, NMT is the next step in machine translation. Compared with SMT, one of the main differences is that NMT can train several features simultaneously, and it does not require previous domain information. In addition, NMT can reduce the word order and syntax mistakes that have been made using SMT. Furthermore, while NMT might take more time to translate a sentence because of the large amount of data involved, it is more efficient than SMT, which has difficulties with languages that have rules that go beyond a six-word unit.
The Advantages of Using NMT
As Google has seen in its use of NMT, the technology has several advantages, including its application of a singular system that can be trained directly on both source and target text. Another significant element of NMT is its capability to automatically fix its parameters throughout its training period. Other benefits include that NMT:
- Efficiently translates grammatically complex languages, including Korean, Japanese, and Arabic.
- Uses algorithms to learn language conventions that come from statistical models, resulting in quicker and better translations.
- Considers the complete sentence, not just a string of words.
- Learns nuances of languages, such as genders, inflection, and formality.
- Helps with applications, including multilingual authoring, translation checking, and multilingual video conferencing.
Applications of NMT
As NMT continues to become more widely used, many industries will benefit. For starters, NMT can improve communication and information that is provided in eLearning programs, allowing presentations and conversations to take place with no language barriers. In the travel industry, NMT can assist service providers as they work to effectively meet the needs of global customers. Furthermore, the e-commerce industry can use it to respond quickly to customers across the globe.
Specific applications of NMT include:
- Being able to interact with anyone across the globe by using NMT technology
- Providing support to customers in their own language
- Enabling trainers and trainees to have successful learning and business training sessions without the hindrance of a language barrier
- Allowing clients of translation service providers to have another means to get top quality translations for their material
- Providing quick turnaround of translations, such as for litigation
- Allowing for real-time translation for any industry
As large companies use neural machine technology, standards will be set for other companies to consider NMT as an option for their clients. The need for global communication and translation will only continue to grow, and NMT is poised to be a significant part of that development.
