NLP vs. NLU vs. NLG: Which is Exploring the Most in Conversational AI?
One cannot deny how far we have come in terms of technology. On that note, one aspect of technology that cannot go unnoticed is that of Artificial Intelligence. The extent to which companies across the globe are relying on AI cannot be merely put into words. Talking about AI, organizations are leaving no stone unturned in making the best possible use of it. One such type of AI that has garnered attention from everywhere across is conversational AI – a technology that users can talk to such as chatbots or virtual agents. When talking about conversational AI, there are three technologies that come into play – Natural Language Processing (NLP), Natural Language Understanding (NLU), Natural Language Generation (NLG). Read on to find out what role do they play in conversational AI.
Natural Language Processing (NLP) enables computers to understand human language. For this, it makes use of methods like computer science, data science, AI, etc. Wondering how NLP works? Well, it converts unstructured data into structured data. By virtue of NLP, computers can read text, hear speech as well as interpret it. The main purpose of NLP is to engage in a human-like conversation. What turns out to be a remarkable feature of NLP is that this language can be coupled with other technologies like of phenomenon modelling, artificial general intelligence in order to come up with unique, more personalized customer engagement.
Natural Language Understanding (NLU) and Natural Language Generation (NLG) are two components of the NLP system.
Natural Language Understanding (NLU) is nothing but understanding the input of the user. It is with the help of NLU that the chatbots are in a position to comprehend what is the meaning of the text given as an input by the user. NLU is associated with a range of tasks – right from categorizing text, gathering news and archiving individual pieces of text to analyzing content. Under NLU, it is possible to determine whether a statement is true or false, keep track of the current state of conversations, automatically answer, systematically identify, extract, and quantify subjective information.
Natural Language Generation (NLG) is nothing but software that aims at producing understandable texts in human languages. Here, the techniques focus on building symbiotic systems that can take advantage of the knowledge and capabilities of both humans and machines. A point to note here is that the input can be any non-linguistic representation of information. Additionally, the output can be any text embodied as a part of a document, report, explanation, or any other help message within a speech stream.
Simply put, NLP, NLU and NLG turn out to be fruitful investments and an integral part of conversational AI. The benefits offered are many. Some of them are – one can engage in conversations 24*7 and need not wait for responses for long for the fact that there is minimal lag time, clear understanding of the user intentions by further mapping it onto relevant responses, provides one-to-one interaction, reduces workload from human operators, and saves a lot of time, among others. NLP, NLU and NLG have made conversational AI easier than ever.
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