Problem Statement
This is the generation where chatbots are replacing humans in customer service. When it is about talking with a human, randomness can be always expected and for a program to understand that randomness, the system needs to be trained for these random possibilities. Listing down every random possibility is often a shortfall for humans. It is just similar to handle the case, you don’t know whether it exists or not. Hvanatge Gives teh best.
Our Solution
Utilizing the Natural Language Processing, our artificial intelligence experts team has designed an AI conversational chatbot that addresses the specific user needs to be developed. This bot is under development so as to replace the previous version (HBot) that is built using Google DialogFlow. This bot utilizes the concepts of Spoken Language Understanding (SLU) for User Intent Identification and Entity Extraction (Slot Filling). Bidirectional RNNs (LSTMs) are used along with the Attention mechanism. Various other model architecture is also under consideration like ALBERT, BERT, Elmo, etc. FastText word embeddings are used as an embedding layer in the model. General entities like number, date, time, location, etc will be detected using spacy and duckling.
Data Science, Machine Learning, Artificial Intelligence
Python 3.x, Haskel (in future for general entity extraction)
Gensim, Keras, Pandas, spacy
