SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models

Anonymous

Keywords: Chatbot, Dialogue Generation, Two-dimensional Word Embedding, Transfer Learning, CNN Model

Abstract: The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach. This paper borrows the idea of Super Characters method and two-dimensional embedding, and proposes a method of generating conversational response for open domain dialogues. The experimental results on a public dataset shows that the proposed SuperChat method generates high quality responses. An interactive demo is ready to show at the workshop. And code will be available at github soon.

TL;DR: Print the input sentence and current response sentence onto an image and use fine-tuned ImageNet CNN model to predict the next response word.

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/superchat-dialogue-generation-by-transfer/code)