Short Text Intent Classification for Conversational Agents

Intent classification is an important and relevant area of research in artificial intelligence and machine learning, with applications ranging from marketing and product design to intelligent communication. This paper explores the performance of various models and techniques for short text intent classification in the context of chatbots. The problem was explored for use within the mental wellness and therapy chatbot application, Wysa, to give improved responses to free-text user input. The authors looked at classifying text samples in-to 4 categories - assertions, refutations, clarifiers and transitions. For this, the suitability of the following techniques was evaluated: count vectors, TF-IDF, sentence embeddings and n-grams, as well as modifications of the same. Each technique was used to train a number of state-of-the-art classifiers, and the results have been compiled and presented. This is the first documented implementation of Arora’s modification to sentence embeddings for real world use. It also introduces a technique to generate custom stop words that gave a significant gain in performance (10 percentage points). The best pipeline, using these techniques together, gave an accuracy of 95 percent.