In natural / casual conversations, people tend to have their own unique style of speaking/ chatting; certain phrases only they use, style of typing, customized grammar, nicknames etc. These 'imperfections' make the dialogue more human; we are able to identify people from their unique style of chatting. Conversational chatbots trained on clean text won't be able to capture these nuances. We propose a method to capture these quirks in the form of a chatbot with a novel NLG (Natural Language Generation) architecture; In the form of a convolutional transformer. WhatsApp chats between two users were cleaned and exported for training the model. The dataset had Unicode characters for foreign lexicon, emojis, and personalised grammar / texting patterns. The model is able to capture these traits in conversation for short responses. © 2021 IEEE.