Natural Language Processing (NLP) is a rapidly growing field of study that focuses on the development of technologies and algorithms that can analyze, interpret, and generate human language. While NLP algorithms have made significant progress in recent years, they are not yet perfect, and there are still many situations in which human intervention is necessary to improve their performance.
While natural language processing (NLP) technologies are assisting financial services in extracting important insights from unstructured data, it is noted that human-in-the-loop solutions are still required (HITL).
According to the RegTech firm, while the algorithms are advanced, they cannot compete with the human brain’s intuition and inventiveness.
It could be explained by comparing an NLP solution to a car travelling on a lengthy journey to help convey the necessity for HITL. While the vehicle contains technological technologies to make the journey more comfortable, such as autonomous navigation and cruise control, the human driver is required to make key judgements and respond to unforeseen events. For example, the automobile may become disoriented, hit obstructions, or even crash.
Yet, HITL can assist NLP solutions adapt and learn from their failures in addition to improving the quality of their insights. Working together, they may discover areas for development and ways to improve the approach.
Here are some reasons why NLP needs human-in-the-loop:
Therefore, the human-in-the-loop approach in NLP can improve the accuracy and reliability of NLP models, reduce the risk of errors and bias, and ensure that NLP technology aligns with social and ethical norms.
In a nutshell, by incorporating human-in-the-loop, financial services businesses may get more accurate and relevant insights, as well as enhance the NLP solution’s performance over time. Because of this ongoing improvement, the sooner you deploy, the better your solution will be in comparison to a competitor’s.