Auto-tagging of Short Conversational Sentences using Natural Language Processing Methods
This work addresses domain-specific tagging for chatbot development, but it is incremental as it applies existing models to a new dataset.
The study tackled the problem of auto-tagging short conversational sentences from customer chat data into ten categories, achieving the best performance using the BERT model.
In this study, we aim to find a method to auto-tag sentences specific to a domain. Our training data comprises short conversational sentences extracted from chat conversations between company's customer representatives and web site visitors. We manually tagged approximately 14 thousand visitor inputs into ten basic categories, which will later be used in a transformer-based language model with attention mechanisms for the ultimate goal of developing a chatbot application that can produce meaningful dialogue. We considered three different state-of-the-art models and reported their auto-tagging capabilities. We achieved the best performance with the bidirectional encoder representation from transformers (BERT) model. Implementation of the models used in these experiments can be cloned from our GitHub repository and tested for similar auto-tagging problems without much effort.