Chatbots have become an integral part of businesses to improve customer experience, as they can provide 24/7 support to customers in an (almost) automated manner. However, to accurately respond to customers enquiries and provide them with the relevant resources or next steps, accurate intent recognition is necessary. Intent recognition involves classifying a short text (sentence or two) and have to classify it into one (or multiple) categories.
Accompanying article: To be(rt) or not to be(rt)
In this project, I'll walk you through how I fine-tune the BERT model for text classification.
This dataset has 14484 observations. Each observation consists of user's query (i.e text e.g. play the song little robin redbreast) has the intent of PlayMusic. There are 7 different intents:
- SearchCreativeWork (e.g. Find me the I, Robot television show),
- GetWeather (e.g. Is it windy in Boston, MA right now?),
- BookRestaurant (e.g. I want to book a highly rated restaurant for me and my boyfriend tomorrow night),
- PlayMusic (e.g. Play the last track from Beyoncé off Spotify),
- AddToPlaylist (e.g. Add Diamonds to my roadtrip playlist)
- RateBook (e.g. Give 6 stars to Of Mice and Men)
- SearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris)
It is hosted on GitHub and is first presented in this paper.
Used DistilBERT to embed text and modeling using Log regression gave us higher accuracy (97%) than using count vectoriser to generate a document-term matrix and modeling using Log reg (95%).
Used BERT to embed text and created 2 fully connected layers using Keras to adapt BERT to our classification task (99%) gave us higher accuracy than using DistilBERT and Log reg.
Feel free to connect with me to find out more. I'd love to hear your suggestions or inputs too. Take carea and stay safe! :) ]