Recent years NLP research has witnessed the record-breaking accuracy
improvement by DNN models. However, power consumption is one of the practical
concerns for deploying NLP systems. Most of the current state-of-the-art
algorithms are implemented on GPUs, which is not power-efficient and the
deployment cost is also very high. On the other hand, CNN Domain Specific
Accelerator (CNN-DSA) has been in mass production providing low-power and low
cost computation power. In this paper, we will implement the Super Characters
method on the CNN-DSA. In addition, we modify the Super Characters method to
utilize the multi-modal data, i.e. text plus tabular data in the CL-Aff
sharedtask.

本篇论文介绍了一种在低功耗、低成本计算平台 CNN-DSA 上实现超级字符方法（Super Characters method）并利用多模数据（text plus tabular data）的方法，以解决在部署 NLP 系统中所面临的高功耗和高成本问题。

低功耗 CNN 加速器设备上多模态情感分析的超级字符方法

Multi-modal Sentiment Analysis using Super Characters Method on  Low-power CNN Accelerator Device

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.

本文提出基于 Super Characters 方法和二维嵌入技术的对话式回复生成方法，实验结果表明提出的 SuperChat 方法可以生成高质量回复，并有工作坊提供交互演示。