SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models
This work addresses dialogue generation for conversational AI systems, but it is incremental as it applies an existing method to a new task.
The paper tackled open-domain dialogue generation by adapting the Super Characters method and two-dimensional word embedding from text classification, resulting in high-quality conversational responses as demonstrated on a public dataset.
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.