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How chatbots use NLP, NLU, and NLG to create engaging conversations

Evolving from basic menu/button architecture and then keyword recognition, chatbots have now entered the domain of contextual conversation. They don’t just translate but understand the speech/text input, get smarter and sharper with every conversation and pick up on chat history and patterns. With the general advancement of linguistics, chatbots can be deployed to discern not just intents and meanings, but also to better understand sentiments, sarcasm, and even tone of voice.

How is that made possible? The answer lies in Natural Language Processing (NLP). Read on to understand what NLP is and how it is making a difference in conversational space.

What is Natural Language Processing (NLP)?

NLP is nothing but an engine for the chatbot to understand the user’s intent in the message and fetch the most appropriate response from its database. Regardless of which language a computer is learning, NLP understands the syntax, semantics, discourse, and purpose of the message to engage in a human-like conversation. It focuses on how we can program computers to process large amounts of natural language data in a way that is productive and efficient, taking certain tasks off the hands of humans and allowing for a machine to handle certain processes. NLP takes it into account the following-

  • Named Entity Recognition (NER) or ‘entity identification’ - locates and classifies named entity mentions in unstructured text into predefined categories
  • Part-of-Speech (POS) tagging or ‘tokenization’ - read a text in some language and assign parts of speech to each word (and other tokens), such as noun, verb, adjective, etc.
  • Text categorization – labels natural language texts with relevant categories from a predefined set
  • Syntactic parsing – analyses a string of symbols to adhere to the rules of a formal grammar

There are two components of an NLP system – Natural Language Understanding (NLU) and Natural Language Generation (NLG). When you input a text into an NLP engine, the meaning or context of the user is deciphered by the NLU construct and the response is generated by NLG. The following equation best explains the relationship between NLP, NLU, and NLG:

NLP = NLU + NLG

Process flow:

User Text -> Chatbot -> NLU -> Meaning deciphered -> NLG -> Response Generated

What is Natural Language Understanding (NLU)?

NLU is understanding the meaning of the user’s input. Primarily focused on machine reading comprehension, NLU gets the chatbot to comprehend what a body of text means. NLU is nothing but an understanding of the text given and classifying it into proper intents.

NLU can be applied to a lot of processes. From categorizing text, gathering news and archiving individual pieces of text to analyzing content, it’s all possible with NLU. Real-world examples of NLU include small tasks like issuing short commands based on text comprehension to some small degree like redirecting an email to the right receiver based on basic syntax and decently sized lexicon.

NLU involves-

  • Natural Language Inference (NLI) and paraphrasing – determines whether a statement is true (entailment), false (contradiction), or undetermined (neutral). This is done by setting a ‘premise’ for the system in the form of a training database
  • Dialogue agent – these managers keep track of the current state of conversations
  • Semantic parsing – converts natural language utterance to a logical form that is understandable by machine
  • Question answering – automatically answers in a natural language
  • Sentiment analysis – uses text analysis, computational linguistics, and biometrics to systematically identify, extract, and quantify subjective information
  • Summarization – shortening the message and emphasizing the major points (intent/entity)

What is Natural Language Generation (NLG)

NLG is a software that produces understandable texts in human languages. NLG techniques provide ideas on how to build symbiotic systems that can take advantage of the knowledge and capabilities of both humans and machines.

The input can be any non-linguistic representation of information and the output can be any text embodied as a part of a document, report, explanation, or any other help message within a speech stream. The knowledge source that goes to the NLG can be any communicative database.

The NLG process is carried out in 7 different steps of document planning, micro-planning, and linguistic realization-

  • Content determination - This enables a conversational AI to decide what to respond - the determination process itself is based on lengthy training data that clearly outlines possible intent and entity sets
  • Discourse planning - This is required to structure the texts based on conceptual grouping and rhetorical relationships
  • Sentence aggregation - This is done to produce large, complex sentences by combining messages in a sentence plan
  • Lexicalisation - Depending upon the developer’s intent, this is done to identify domain-centric jargons
  • Referring expression generation - This covers the usage of reference - nouns, pronouns, definite description, etc.
  • Syntactic and morphological realization - The rudimentary concepts of grammar like morphology and syntax are fed into the system at this stage
  • Orthographic realization - Last for language generation is the correct use of punctuation and casing and typographic requirements like font size

Conversations with a meaning

Deploying a rule-based chatbot can only help in handling a portion of the user traffic and answering FAQs. NLP (i.e. NLU and NLG) on the other hand, can provide an understanding of what the customers “say”. Without NLP, a chatbot cannot meaningfully differentiate between responses like “Hello” and “Goodbye”. They will both be nothing more than text-based user inputs.

NLP-driven intelligent chatbots can, therefore, improve the customer experience significantly. Customers all around the world want to engage with brands in a bi-directional communication where they not only receive information but can also convey their wishes and requirements. Given its contextual reliance, an intelligent chatbot can imitate that level of understanding and analysis well. Within semi-restricted contexts, it can assess the user’s objective and accomplish the required tasks in the form of a self-service interaction. Such a chatbot builds a persona of customer support with immediate responses, zero downtime, round the clock and consistent execution, and multilingual responses.

With Chatbots, Possibilities are Endless

NLP based chatbots not only increase growth and profitability but also elevate customer experience to the next level all the while smoothening the business processes. Together with Artificial Intelligence/ Cognitive Computing, NLP makes it possible to easily comprehend the meaning of words in the context in which they appear, considering also abbreviations, acronyms, slang, etc. This offers a great opportunity for companies to capture strategic information such as preferences, opinions, buying habits, or sentiments. Companies can utilize this information to identify trends, detect operational risks, and derive actionable insights.

NLP, NLU, and NLG can prove to be fruitful investments as they provide-

  • Immediate assistance: Like a normal chatbot, they can engage in conversations 24*7 with responses on the fingertips and minimal lag time
  • Efficient service: Precise understanding of user intentions and mapping it onto relevant responses
  • More Human-like engagement: Personalized, one-to-one experience, in a conversational style is delivered creating a better, humane impression
  • Cost and time saving: Reduce workload from human operators even for complex tasks and save time

Additionally, NLP can be coupled with budding technologies like phenomenon modelling, intelligent decisions, reasoning, and autonomy to artificial general intelligence (AGI) to weave an even more personalized and unique customer engagement. It can identify spelling and grammatical errors and interpret the intended message despite the mistakes. This can have a profound impact on a chatbot’s ability to carry on a successful conversation with a user.