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

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.

5 pages, 2 figures, 1 table. Accepted by CVPR2019 Language and Vision Workshop

References in corpus (10)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Neural Machine Translation by Jointly Learning to Align and Translate
• Sequence to Sequence Learning with Neural Networks
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Squeeze-and-Excitation Networks
• Effective Approaches to Attention-based Neural Machine Translation
• Beam Search Strategies for Neural Machine Translation
• MRAM Co-designed Processing-in-Memory CNN Accelerator for Mobile and IoT Applications
• Squared English Word: A Method of Generating Glyph to Use Super Characters for Sentiment Analysis
• Generating Multiple Diverse Responses for Short-Text Conversation

Cited by in corpus (4)

• System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications
• SuperCaptioning: Image Captioning Using Two-dimensional Word Embedding
• Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device
• GnetSeg: Semantic Segmentation Model Optimized on a 224mW CNN Accelerator Chip at the Speed of 318FPS