Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality

TL;DR

This paper explores methods for developing autonomous task bots that can learn, adapt, and improve their performance in dynamic environments with minimal human input.

Contribution

It introduces novel techniques for self-learning in task bots, enhancing their adaptability, extensibility, and factual accuracy.

Findings

Self-learning techniques improve bot adaptability.

Bots can extend functionalities with minimal human intervention.

Enhanced factuality in task bots through autonomous learning.

Abstract

Developing adaptable, extensible, and accurate task bots with minimal or zero human intervention is a significant challenge in dialog research. This thesis examines the obstacles and potential solutions for creating such bots, focusing on innovative techniques that enable bots to learn and adapt autonomously in constantly changing environments.