One System to Rule them All: a Universal Intent Recognition System for Customer Service Chatbots

TL;DR

This paper presents a universal intent recognition system for customer service chatbots, capable of identifying common intents across various systems using advanced NLP models, thereby streamlining chatbot training and deployment.

Contribution

The paper introduces a novel universal intent recognition system trained on 11 common intents across 28 chatbots, utilizing state-of-the-art embeddings and deep learning classifiers.

Findings

Achieves up to 80.4% balanced accuracy in intent recognition.

Effectively recognizes both short and long user requests.

Identifies similar intents with some misclassification, such as farewells and positive comments.

Abstract

Customer service chatbots are conversational systems designed to provide information to customers about products/services offered by different companies. Particularly, intent recognition is one of the core components in the natural language understating capabilities of a chatbot system. Among the different intents that a chatbot is trained to recognize, there is a set of them that is universal to any customer service chatbot. Universal intents may include salutation, switch the conversation to a human agent, farewells, among others. A system to recognize those universal intents will be very helpful to optimize the training process of specific customer service chatbots. We propose the development of a universal intent recognition system, which is trained to recognize a selected group of 11 intents that are common in 28 different chatbots. The proposed system is trained considering state-of-the-art word-embedding models such as word2vec and BERT, and deep classifiers based on convolutional and recurrent neural networks. The proposed model is able to discriminate between those universal intents with a balanced accuracy up to 80.4\%. In addition, the proposed system is equally accurate to recognize intents expressed both in short and long text requests. At the same time, misclassification errors often occurs between intents with very similar semantic fields such as farewells and positive comments. The proposed system will be very helpful to optimize the training process of a customer service chatbot because some of the intents will be already available and detected by our system. At the same time, the proposed approach will be a suitable base model to train more specific chatbots by applying transfer learning strategies.