Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System
This work addresses data scarcity in task-oriented dialogue systems for children's education, though it is incremental as it builds on existing data augmentation methods.
The paper tackled the problem of limited datasets for Natural Language Understanding (NLU) in multimodal dialogue systems for children learning math, by using data augmentation with paraphrase generation and entity extraction, resulting in improved performance for Intent Recognition tasks.
Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time. Our goal is to build Artificial Intelligence (AI) systems that can assist children in their learning process. Within such complex frameworks, Spoken Dialogue Systems (SDS) are crucial building blocks to handle efficient task-oriented communication with children in game-based learning settings. We are working towards a multimodal dialogue system for younger kids learning basic math concepts. Our focus is on improving the Natural Language Understanding (NLU) module of the task-oriented SDS pipeline with limited datasets. This work explores the potential benefits of data augmentation with paraphrase generation for the NLU models trained on small task-specific datasets. We also investigate the effects of extracting entities for conceivably further data expansion. We have shown that paraphrasing with model-in-the-loop (MITL) strategies using small seed data is a promising approach yielding improved performance results for the Intent Recognition task.