Improving Low-resource Question Answering by Augmenting Question Information

Andong Chen, Yuan Sun, Xiaobing Zhao, Rosella P. Galindo Esparza, Kehai Chen, Yang Xiang, Tiejun Zhao, Min zhang

Submission Type: Regular Short Paper

Submission Track: Question Answering

Submission Track 2: Resources and Evaluation

Keywords: Question Answering, Data augmentation, Low-resource domains

Abstract: In the era of large models, low-resource question-answering tasks lag, emphasizing the importance of data augmentation - a key research avenue in natural language processing. The main challenges include leveraging the large model's internal knowledge for data augmentation, determining which QA data component - the question, passage, or answer - benefits most from augmentation, and retaining consistency in the augmented content without inducing excessive noise. To tackle these, we introduce PQQ, an innovative approach for question data augmentation consisting of Prompt Answer, Question Generation, and Question Filter. Our experiments reveal that ChatGPT underperforms on the experimental data, yet our PQQ method excels beyond existing augmentation strategies. Further, its universal applicability is validated through successful tests on high-resource QA tasks like SQUAD1.1 and TriviaQA.

Submission Number: 1839