Data Augmentation for Conversational AI
It tackles the problem of limited training data for researchers and practitioners in conversational systems, but it is incremental as it reviews existing methods rather than introducing new ones.
This tutorial addresses the challenge of data scarcity in conversational AI by providing an overview of data augmentation approaches, highlighting recent advances in conversation generation and evaluation paradigms.
Advancements in conversational systems have revolutionized information access, surpassing the limitations of single queries. However, developing dialogue systems requires a large amount of training data, which is a challenge in low-resource domains and languages. Traditional data collection methods like crowd-sourcing are labor-intensive and time-consuming, making them ineffective in this context. Data augmentation (DA) is an affective approach to alleviate the data scarcity problem in conversational systems. This tutorial provides a comprehensive and up-to-date overview of DA approaches in the context of conversational systems. It highlights recent advances in conversation augmentation, open domain and task-oriented conversation generation, and different paradigms of evaluating these models. We also discuss current challenges and future directions in order to help researchers and practitioners to further advance the field in this area.