Incremental Learning from Scratch for Task-Oriented Dialogue Systems

By Weikang Wang, Jiajun Zhang, Q. Li, Mei-Yuh Hwang, Chengqing Zong, Zhifei Li. 2019

23 citations recorded by the source corpus.

Clarifying user needs is essential for existing task-oriented dialogue systems. However, in real-world applications, developers can never guarantee that all possible user demands are taken into account in the design phase. Consequently, existing systems will break down when encountering unconsidered user needs. To address this problem, we propose a novel incremental learning framework to design task-oriented dialogue systems, or for short Incremental Dialogue System (IDS), without pre-defining the exhaustive list of user needs. Specifically, we introduce an uncertainty estimation module to evaluate the confidence of giving correct responses. If there is high confidence, IDS will provide responses to users. Otherwise, humans will be involved in the dialogue process, and IDS can learn from human intervention through an online learning module. To evaluate our method, we propose a new dataset which simulates unanticipated user needs in the deployment stage. Experiments show that IDS is robust to unconsidered user actions, and can update itself online by smartly selecting only the most effective training data, and hence attains better performance with less annotation cost.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Dialogue & Conversational AI, Meta & Continual Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hang Su
2. Jun Zhu
3. Tom Young
4. Liyuan Wang
5. Xingxing Zhang
6. Jinjie Ni
7. Vlad Pandelea
8. Fuzhao Xue

In Computer Vision and Pattern Recognition

1. Hang Su
2. Jun Zhu
3. Liyuan Wang
4. Xingxing Zhang
5. Tom Young
6. Jinjie Ni
7. Vlad Pandelea
8. Fuzhao Xue

In Meta & Continual Learning

1. Xingxing Zhang
2. Liyuan Wang
3. Hang Su
4. Jun Zhu
5. Simone Scardapane
6. Valerio Marsocci
7. Liyuan Wang
8. Hongwei Yan

Citations in the corpus

• A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “The applications of continual learning in NLP are also highly diverse, such as dialogue system [130], [131], [260], [260], [282], [298], [437], text classification [87], [172], [325], [460], sentence generation [246], [298], [499], relation learning [343], [357], [455], [465], neural machine transla”
• Recent advances in deep learning based dialogue systems: a systematic survey (2021, methodology): “Wang et al. (2019a) proposed an incremental learning framework to train their end-to-end taskoriented system.”