Contents
Unsupervised NLP is gradually gaining the spotlight as a powerful tool for training chatbots. Tap on the mechanism behind this new buzzword and discover how it will soon become prevalent.
Nowadays, emerging in conversations and seeking answers from chatbots has become a highly common practice. Current chatbots are mostly rule-based. They have proven to be useful in handling and responding to simple inquiries or frequently asked questions, thus saving time and effort for human staff.
However, when customers demand a higher level of sophistication in their experience, supervised NLP starts to fall short and attention is diverted to a rising technology: unsupervised NLP.
Supervised NLP – The conventional approach to training and deploying chatbots
Natural Language Processing (NLP) is a subset of Artificial Intelligence (AI) that enables computers to understand and interpret human language in both written and verbal forms. NLP has two components: Natural Language Understanding (NLU) and Natural Language Generation (NLG). The former performs syntactic and semantic analysis on the given text and speech to determine its meaning. Meanwhile, the latter allows computers to produce natural text responses based on the given semantics.
Chatbots with supervised NLP systems can provide rapid and relatively effective conversation with humans if equipped with a sufficiently large database. However, when virtual customer engagement increases, they start to fall short of expectations.
Their function relies almost solely on abundantly categorized data sources, which are expensive and time-consuming to construct in the first place. This feature can be a major weakness. When new variables emerge and experts must retrain the system to help them deal with such unfamiliar inputs, it causes additional man-hours and efforts. As a result, in the far future, supervised NLP can become obsolete and fail to catch up with growing complexity in human-machine interaction.
Unsupervised NLP – A more effective approach to tailoring excellent bot responses
Concept explained
The latest generation of chatbots is fueled by artificial intelligence and built upon self-learning algorithms. Unsupervised NLP chatbots independently analyze and classify raw data to generate responses, hence taking up the most challenging part of the process.
Thanks to this difference, post-deployment supervision from an expert are reduced significantly and the set-up time turns from months to days, or possibly even hours. As a result, it is considered the latest development in the field of learning, adapting to, and replicating human language.
The concerns of deploying an unsupervised system
The biggest obstacle of employing unsupervised NLP-based chatbots, especially for SMEs, is cost. Indeed, a more computationally complex system means a larger initial investment. Many start-ups and companies of small and medium sizes find it virtually unaffordable. Also, the process will involve high-profile and skilled scientists in the set-up stage, which can be out-of-reach for smaller institutions.
Additionally, unsupervised chatbots may generate less accurate outcomes than their supervised counterparts. It can be compared to a black box when there is less human control and greater exposure to the unknown. If something goes wrong, intervention might be difficult.
Do its benefits outweigh the disadvantages?
Unsupervised NLP’s scalable and adaptive nature, in the long run, is certainly worthy of the initial investment.
Though small and medium-size companies now mostly deal with simple, repeated requests, customers will want more natural, “human-like” responses in the future. In a highly competitive marketplace, they will prefer institutions offering a higher degree of personalization and contextual understanding. This level of sophistication can only be met by the contribution of unsupervised, AI-powered systems. Otherwise, organizations may risk losing key customers due to robotic and unnatural responses.
In terms of cost, the rapid development of AI and other technologies means that new approaches, such as the application of unsupervised NLP, will soon be more accessible. It is, therefore, preferable that business leaders catch up with the latest digital trends and prepare to scale their organizations accordingly.
Hybrid NLP – A new milestone in the post-Covid era?
As we enter a new post-Covid era, conversational AI opens up new opportunities to increase revenue and compete effectively. Combining the accuracy of supervised NLP and the highly adaptive nature of unsupervised NLP, many scientists and organizations are taking Hybrid NLP into consideration.
The hybrid approach involves human expertise that provides guidance during the analysis process so it can scale while limiting inaccuracies. Simultaneously, ML and AI reduce human effort by relying on semi-supervised learning to automate data labeling.
The most significant benefit to this approach is that it can be built without an enormous set of training data. It also offers more transparency and controllability, since human staff can track the system’s performance against business goals. With the involvement of unsupervised NLP, the system is notably more flexible and achieves greater resource efficiency. Therefore, it is an ideal choice for tailoring better conversational experiences. The hybrid approach, gradually, will be seen as the best practice for organizations when it comes to analyzing unstructured data inputs and generating helpful responses.
Consequently, it will characterize the most advanced AI chatbots, as it balances the two technologies for optimal outcomes, taking up tasks both with and without labeled data. In the future, “unsupervised yet controlled learning” is the new buzzword.
Closing thoughts
The prospective growth in volumes and contextual richness of data is inevitable. To retain customers, maintain a competitive advantage, and achieve sustainable growth, business leaders, therefore, should be aware of the newly emerging technologies and prepare for future adaptation.
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