Human communication is complex and multifaceted, and therefore difficult for chatbots to accurately mirror. As humans, we naturally adapt our tone and communication style to suit the nature and context of our conversations. Researchers in the field of natural language processing (NLP) are studying the characteristics of human conversation and how it relates to psychological aspects.
With this aim, recent advancements in NLP technology are enabling chatbots to more accurately mimic natural human interaction for enhanced customer service. Read on to discover how to improve chatbot interaction with NLP.
Characteristics of Human Conversation
During conversation, we are continually picking up different psychosocial cues from our conversational partner. We either consciously or subconsciously respond to these cues and adapt our tone and communication style to have the desired effect on our relationship with the communicatee.
For example, if your partner is upset that you haven’t unloaded the dishwasher, you will naturally adjust your tone to maintain harmony and resolve the issue while preventing conflict. If your manager asks you to work overtime and you have other commitments, you will again change your tone to be assertive while still communicating your commitment and dedication to your work.
In both of these instances, your communication style will reflect the social context and the emotionality of the situation.
In customer service, this adaptability is particularly relevant and important. In their day-to-day work, customer service agents have to respond to customers in different emotional states who need assistance in various ways. Customer service agents have to respond to simple informational requests, walk customers through various administrative procedures and help resolve issues for customers who have had a negative experience.
Throughout their interactions, their job is to restore, maintain and build relationships for enhanced customer satisfaction and ideally, loyalty.
For chatbots to mimic this ability effectively, you need the help of advanced AI that is specifically trained to pick up on psychological cues in communication.
The first step to imitating natural communication is to understand its defining features. Our researchers at Symanto have outlined five key psycholinguistic characteristics of human interaction. Our technology is programmed to pick up on these characteristics to enable enhanced communication.
The five characteristics are:
1. Emotionality: During conversation, our emotional state is highly responsive and reactive to triggers within the interaction. Our emotions change rapidly and vary in intensity depending on how we are spoken to.
In conversation, it is important that the emotional party feels heard and validated with an empathetic response. This is something that chatbots often get wrong. It’s vital for AI to accurately identify emotionality in order to be able to respond appropriately.
The next four characteristics relate to the communication style of the customer. People have individual preferences about how they choose to share information. In human-human conversation we instinctively match our communication style to the person we are talking to.
2. Self-revealing: People with a self-revealing communication style talk openly about their personal and subjective experiences.
E.g. “I’ve been buying your products since I was a kid and I’ve never had a problem until now.”
3. Fact-orientated: Fact-oriented communicators prefer to share facts, data and objective statements.
E.g. “The battery drains within three hours of standard use.”
4. Action-seeking: People with an action-seeking communication style make direct or indirect requests and suggestions, expecting action from other people.
E.g. “Can you update your software so other apps can access the information?”
5. Information-seeking: Information-seeking communicators ask direct or indirect questions searching for information.
E.g. “Where can I find your nearest store?”
At Symanto, we have developed models and trained our technology to analyse text and rapidly identify communication style preferences and emotionality in communication. Our technology stands out from other NLP models in that it is effective even on very short excerpts of text. This is particularly useful when it comes to creating chatbots that need to respond to communication cues as soon as possible during short interactions.
How to Improve Chatbot Effectiveness With NLP technology
First and foremost, NLP technology helps chatbots capture customer sentiment and emotion through implicit cues (i.e. without explicitly asking the customer how they feel).
Symanto technology is highly effective at accurately deciphering the five psycholinguistic characteristics in even very small amounts of text data.
With this information, you can then train chatbots to respond appropriately, validate the customers’ emotions and work to change any negative emotional states for the better.
Since our NLP technology is highly effective at identifying communication style preferences, it also opens up possibilities for creating chatbots with personalised responses to match the tone of the customer.
For example, self-revealing types are more likely to respond better to conversational communication, whereas fact-oriented communicators would most likely prefer to-the-point, factual responses.
When a robot doesn’t understand you, it’s frustrating. Anyone who has ever struggled with in-car voice recognition systems can identify with this fact. So when your already-frustrated customers come to you seeking a resolution, the last thing they want is to deal with inaccurate chatbots.
It’s vital for your chatbot to be able to recognise exactly what your customer is requesting or seeking answers to. Symanto technology enables accurate topic detection in any industry with no prior model training required.