Machine translation (MT) has undergone significant evolution through the incorporation of artificial intelligence (AI), which uses neural systems to simulate human translations via machine learning. This advancement has addressed several shortcomings, particularly in natural language processing (NLP).
Despite these advances, machine translation still falls short of the reliability of human translations. Therefore, human supervision is crucial to achieving the highest quality in neural machine translation processes. One of the best forms of human-machine collaboration is the human-in-the-loop (HITL) model.
Human-in-the-loop (HITL) is a strategy used in artificial intelligence processes to enhance machine learning. This methodology integrates human assessment and input into the development and operation of an AI system, creating a feedback loop that refines the automated model. AI algorithms train themselves using vast amounts of data, continually learning to provide texts with increasingly accurate results.
Both artificial and human intelligence have their limitations and strengths, and by combining them, we can leverage the positive aspects of each to implement the positive aspects of each and achieve better outcomes. This collaborative approach is known as HITL.
In other words, human intelligence must intervene when a machine encounters difficulties in solving a problem. Humans adjust the initial data of the learning algorithm, and based on this constant feedback, the algorithm improves its predictions and decisions, achieving progressively better results.
Often, there is algorithmic uncertainty, meaning a lack of confidence in the final result. Therefore, direct human interaction is necessary to create continuous learning and improve the quality of translations.
Human intervention enables translation algorithms to better align with human logic, resulting in more accurate translations. This method combines advanced machine capabilities with human reasoning, thus reducing the errors of the artificial intelligence algorithm.
For instance, a software company that wants to translate its website may choose to use a machine translation algorithm to save time. By translating the Spanish phrase “Nos comprometemos a ofrecer soluciones tecnológicas de vanguardia para impulsar a las empresas.” the algorithm could convert it into English as “We are committed to offering cutting-edge technology solutions to drive businesses forward.”
While this translation is technically correct, it may sound stiff or unnatural in English. When a human translator intervenes, they would adjust the phrase to match the culturally appropriate style and tone of the target language. So, you might rephrase it as: “We are committed to empowering businesses with cutting-edge technology.”
This intervention by the human translator not only ensures the accuracy of the message but also optimizes the cultural adaptation and impact of the content, achieving a more effective and appealing result for the target audience.
Human-machine interaction is used in two main phases.
Some of the main benefits of human-in-the-loop are as follows:
While there are many advantages to human-in-the-loop systems, it is also important to recognize that they can face challenges and limitations that hinder the process if not addressed properly:
The human-in-the-loop approach to artificial intelligence and machine translation is crucial to overcoming the current limitations of machines. Blending human intelligence with artificial intelligence leads to a continuous and measurable improvement in the quality of translations and other automated processes, thus ensuring optimal and reliable performance.
We hope you have found this information useful. We will be happy to provide you with more details about the different aspects of translation. Do not hesitate to contact us or visit our blog for more information.