1. Paper title

Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment

2. link

https://www.aclweb.org/anthology/2020.acl-main.57.pdf

3. 摘要

Existing end-to-end dialog systems perform less effectively when data is scarce. To obtain an acceptable success in real-life online services with only a handful of training examples, both fast adaptability and reliable performance are highly desirable for dialog systems. In this paper, we propose the Meta-Dialog System (MDS), which combines the advantages of both meta-learning approaches and human-machine collaboration. We evaluate our methods on a new extended-bAbI dataset and a transformed MultiWOZ dataset for lowresource goal-oriented dialog learning. Experimental results show that MDS significantly outperforms non-meta-learning baselines and can achieve more than 90% per-turn accuracies with only 10 dialogs on the extendedbAbI dataset.

4. 要解决什么问题

当训练数据少时，要求对话系统非常需要快速适应性和可靠性能。

5. 作者的主要贡献

Meta-Dialog System (MDS)：将meta-learning和人机协作相结合。

6. 得到了什么结果

extended-bAbI dataset和transformed MultiWOZ dataset
性能基于Baseline
只使用10个对话训练就能达到90%的准确率。

7. 关键字

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