Power of Human Review in Machine Translation
So what makes machine translation so easy?
For one, it’s the ability to process large amounts of text quickly and appropriately. Machine translation systems can translate text in short duration, allowing for real-time communication across languages.
Another key aspect of machine translation is its ability to learn from data pattern. Machine translation systems can be trained on vast amounts of text data, allowing them to adapt to new languages, dialects, and styles. This means that machine translation systems can improve over time.
The aspect of machine translation is its ability to transcend language barriers. Machine translation systems can translate text into multiple languages, allowing people to communicate with each other across linguistic and cultural boundaries. This has opened up new opportunities for global collaboration, business, and personal relationships.
Machine translation is not just a theoretical concept – it’s being used in a wide range of real-world applications. From customer service chat bots to online content platforms, machine translation along with human interventions revolutionizing the way we communicate.
Machine translation was incepted in the 1950s. It uses algorithms to translate text based on the data structure and output required for the target audience.
Machine translation has evolved into a variety of systems, each with its own set of pros and cons. It can be classified into 3 most common methods like:
- Rule-based Machine Translation (RBMT)
- Statistical Machine Translation (SMT)
- Neural Machine Translation (NMT)
Rule-based Machine Translation (RBMT):
It uses pre-defined rules and dictionaries to translate text. These systems were limited in their ability to understand the nuances of language and often produced translations that were awkward or unintelligible. RBMT technique incorporates the application of broad language rules in three stages: Direct, transfer and Interlingual approach.
Statistical Machine Translation (SMT):
The advent of statistical machine translation (SMT) in the 1990s marked a significant improvement in machine translation. SMT used large amounts of data to train statistical models that could learn patterns and relationships in language. This enables machine translation systems to generate accurate and natural-sounding translations.The approach used in SMT involves three distinct methods: Word-based, Phrase-based, and Syntax-based translation.
Neural Machine Translation (NMT):
Neural Machine Translation (NMT) was initiated in 2010s; it uses deep learning techniques, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, to learn from vast amounts of text data. This approach enables machine translation systems to capture subtle nuances in language, including context, idioms, and cultural references.
Conclusion:
While machine translation has the potential to enhance human productivity, its implementation is contingent upon a thorough evaluation of the associated research, development, and maintenance costs. To achieve a positive profit and loss (PnL) outcome, it is essential to develop a cost-effective financial model. Furthermore, I suggest exploring collaborative solutions that leverage the strengths of both machine intelligence and human expertise to optimize efficiency and accuracy, thereby driving a seamless and effective throughput.
Disclaimer:
The purpose of this blog adds value to translation process and this may not be taken as a comprehensive or authentic content. This blog “Power of Human Review in Machine Translation” is intended to provide general information and insights based on our research and real time understanding.
