{'fraction_non_alphanumeric': 0.02528977871443625, 'fraction_numerical': 0.0, 'mean_word_length': 5.089743589743589, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Context Sentences to Single Vector Compression using Convolutional Transformers for Deep Learning based NLG Tasks

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.