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

arXiv: https://arxiv.org/abs/1905.05698 · HTML: https://mlsanity.com/paper/1905.05698/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2019.

## Built on by (most cited first, 2 of 2)

- [GnetSeg: Semantic Segmentation Model Optimized on a 224mW CNN Accelerator Chip at the Speed of 318FPS](https://mlsanity.com/paper/2101.10444/) (2021)
- [GnetDet: Object Detection Optimized on a 224mW CNN Accelerator Chip at the Speed of 106FPS](https://mlsanity.com/paper/2103.15756/) (2021)

## Abstract (arXiv)

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.