Why NLP needs human-in-the-loop

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.

Why NLP Needs Human-In-The-Loop​

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:

  1. Ambiguity in language: Human language is full of ambiguity, and NLP algorithms often struggle to accurately interpret the meaning of words or sentences. For example, consider the sentence “I saw her duck.” Does this mean that the speaker saw a duck that belongs to her, or did the speaker see her physically duck down to avoid something? In such cases, a human must intervene to resolve the ambiguity and provide the correct interpretation.
  2. Variability in language: Language can vary greatly depending on the context and the speaker. NLP models need to be trained on large and diverse datasets to account for this variability, but they may still struggle to recognize less common expressions, dialects, or neologisms. Humans can provide context and expertise that can help NLP algorithms better understand and analyze these variations.
  3. Misclassification errors: NLP models can make mistakes, such as misclassifying text, due to lack of training data or model biases. Humans can help correct these errors by identifying them and providing feedback to improve the model’s accuracy.
  4. Social and ethical considerations: NLP models can potentially perpetuate bias, discrimination, or misinformation if not properly designed and tested. Humans can provide critical insight and oversight to ensure that NLP algorithms are fair, transparent, and aligned with ethical standards.

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.