# Incremental Learning from Scratch for Task-Oriented Dialogue Systems

## TL;DR

This paper introduces an incremental learning framework for task-oriented dialogue systems that can adapt to unforeseen user needs in real-time, reducing the need for exhaustive pre-programming and minimizing annotation costs.

## Contribution

The paper presents a novel IDS framework with uncertainty estimation and online learning, enabling dialogue systems to adapt dynamically to unanticipated user demands without pre-defined needs.

## Key findings

- IDS is robust to unconsidered user actions.
- It can update itself online with minimal training data.
- Achieves better performance with lower annotation costs.

## Abstract

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.