The Role of NLP in Conversational AI
We live in a moment of digital transformation where governments, services and brands are using new technologies to reach out to consumers. Increasing consumer demands for 24/7, omnichannel services have furthered the need to develop automated or self-service processes to meet these requirements.
Chatbots are a solution to these requests. However, a common frustration point when people use chatbots is that the bot doesn’t always understand what they want to say. This isn’t a fault of the technology as a solution, but a result of the bot’s Natural Language Processing design and development not being up to standard.
But what is NLP and how important is its role in delivering top quality Conversational AI solutions? This article will focus on the role NLP and its elements play in the creation of a robust CAI platform.
What is the link between Conversational AI and Natural Language Processing?
Conversational AI (CAI) is an advanced way of offering a conversational experience through digital technologies that mimics real-life conversations with people. It does so by using rich data sets, algorithms and linguistic knowledge to provide users with engaging, multilingual experiences on multiple platforms and devices.
Conversational AI provides scalable, integrated and advanced dialogue systems that deliver personalized and contextual conversations to extract meaning from natural sentence structures. Conversational AI is an entire solution that incorporates numerous technologies to provide an optimal digital conversational experience.
Ultimately, Conversational AI is the predominant form of AI in business right now, and its success holistically spans around various related areas beyond deep learning and machine learning state-of-the-art systems, data privacy and analytics, as well as traditional, complex dialogue management techniques like context awareness, topic awareness, topic switching, interruption handling, anaphora resolution etc.
On the other hand, Natural Language Processing (NLP) is a subfield of Artificial Intelligence that focuses on helping computers understand human language. Natural Language Processing is also an important component of Conversational AI. By using techniques to understand the intent in human language, NLP breaks down sentence structures to deliver the best response and improve customer experience by understanding what the user says.
Natural Language Processing: Measuring and Categorizing Human Language
Machines are quick, consistent and highly efficient, but they do not excel in understanding humans nor the context or the reasoning behind what they say.
While being different to each other, Conversational AI and Natural Language Processing both enable human-like interactions between people and connected devices by facilitating contextual and comprehensive cooperation between humans and machines. This can be done across a variety of modalities, like voice, text, tapping, gestures or facial expressions, and improves one-directional or back-and-forth interactions between humans and computers.
To understand human language, machines need to listen, process, break down, analyze and categorize human text and speech.
In order to achieve this, Natural Language Processing can dissect any volume of text into sentences and words and then processing and categorizing it. Here are some of the core tasks functions NLP can carry out:
Normalization: A process that converts a list of words into uniform sequences such as lowercase.
Tokenization & Sentence Splitting: The task of splitting a text into its constituent parts, for example, words or sentences.
Spelling Correction & Misspelling Tolerance: Correct spelling mistakes or accept typos while understanding what word was intended.
Part of Speech (POS) tagging & Morphological Analysis: defining the attributes of each word, for example, whether a word is a noun or a verb, and then tagging this for future reference.
Language Detection: identifying the language that is being used.
Sentiment & Intensity Detection: identifying words that indicate sentiment and/or intensity within a text.
Advanced Natural Language Processing Capabilities
When further examining NLP, numerous capabilities stand out beyond analyzing the linguistic components in a text. We have listed some below:
Topic Modeling: topic modeling helps uncover hidden topics from large collections of documents. While it is an unsupervised method of NLP, where models are trained without pre-tagging or labeling, it can be used to discover latent patterns in unstructured data and discover unnoticed correlations in customer behavior.
Text Categorization: as mentioned, NLP can classify texts in numerous ways, so that they are easier to process and analyze. This categorization can provide insights about the user intent and also serve to detect and categorize undesirable patterns and spam.
Text Clustering: texts can be grouped based on their contextual similarities. This helps better understand the key issues under a specific domain.
Information Extraction: large amounts of unstructured data can be parsed and keywords about an issue are extracted and collected.
Named Entity Resolution: a more specific form of parsing seeking exact names, brands, locations, etc.
Relationship Extraction: NLP can identify and extract semantic relations between entities or brands, determining, for example if two brands have a parent-agency relationship, or if they are partners depending on the context.
The Elements that Comprise and Extend Natural Language Processing
Conversational AI and NLP allow chatbots to communicate with end-users in a way that mirrors human conversation. There are many different components that overlap and extend NLP’s capabilities.
Natural Language Understanding (NLU)
Natural Language Understanding focuses on enabling machines to understand spoken, written and gestural communication. It is the facet of NLP that focuses on the capabilities highlighted previously. Understanding intent is more than detecting what phonetic sounds or symbols are added in to make a word, but of interpreting what the meanings are and what it is the user wants to achieve.
With NLP, people using a chatbot don’t have to rely on exact keyword matches. They can use complex sentences, in different regional and sociocultural variations, using idioms or broken grammar, and NLP subsequently breaks down each sentence, settling linguistic ambiguities and providing syntactic and semantic comprehension.
NLU provides the building blocks to interpret human language and the message being said. It recognizes patterns and establishes what the user is trying to say, even when a similar message is expressed in different ways, to deliver an appropriate response.
Conversational AI takes natural language processing (NLP) and natural language understanding (NLU) to the next level. It allows enterprises to create advanced dialogue systems that utilize memory, personal preferences and contextual understanding to deliver a proactive natural language interface.
The best conversational AI platforms offer lexical resources that cover common terms, expressions, vocabulary and phrases for a specific language and domain. These serve as the basis upon which dialogue flows are constructed. When combined with AI algorithms, they can determine the many different ways a phrase or question can be structured.
Chatbots will still have their limitations, as they are typically designed to carry out specific tasks related to their customers. So, for example, bank bots will be programmed to understand information and terms related to financial services and contextual information about a customer but will not know the cheapest travel tickets or how to suggest the best broadband deal.
While establishing boundaries can limit the possibility of having generic conversations with a chatbot, this guarantees more profound contextualization and understanding of intent within the chatbot’s key functions.
Natural Language Generation (NLG)
Natural Language Generation is another component that combines with NLU in order to support NLP’s goals. Understanding and interpreting a message is essential. But real conversations require a meaningful response. NLG explores the data, integrated back-end systems and third-party databases to create a response that incorporates additional parameters that will give the final message a fresh, accurate and personalized effect.
NLG is responsible for creating sentences from data, or templates, structuring them into a coherent narrative, or generating a synopsis of large volumes of text. The efficient use of contextual data will ultimately provide an enhanced humanlike experience.
Dialogue Management (DM)
Dialogue Management determines how a chatbot can select the correct reply with the information that it has based on what has been said, the knowledge the chatbot has, and the goals that have been defined, in order to guide the conversation.
Conversational AI holds all these components together.
Conversational AI Uses NLP to Deliver the Best Customer Journeys
Today, people use numerous devices to connect with each other, carry out transactions and interact with their favorite brands. Along with the growing need to provide accurate and efficient automated self-services, businesses must ensure that customer experience, a major brand differentiator, is of the highest quality.
When customers can use their own words when talking to a chatbot and receive immediate and accurate responses, call center volumes are decreased, saving resources, revenue and time for agents to focus on more complex issues, while maintaining high customer satisfaction rates.
Conversational AI platforms like Teneo can make the most of the elements of NLP, and help both the customer and the human agent, by providing aggregated insights, summaries and suggesting possible personalized responses by tracking data and delivering efficient resolutions.
Conversational experiences cannot be created via stand-alone tasks. The best Conversational AI implementations require end-to-end CAI platforms, covering every core module from NLP tasks, to NLU handling, Dialogue Management and NLG. Additionally, they provide data privacy and analytics capabilities.
With the increasing use of new technologies, voice assistants and conversational platforms, NLP is crucial in determining that CAI platforms deliver conversations that are accurate, personalized and as human-like as possible.
Gartner states that “through 2028, the user experience will undergo a significant shift in how users experience the digital world. Conversational platforms are changing the way people interact with the digital world.” Enterprises must not be caught offside and seek the best platforms to stay ahead in the market.