Andong Chen


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Improving Low-resource Question Answering by Augmenting Question Information
Andong Chen | Yuan Sun | Xiaobing Zhao | Rosella Galindo Esparza | Kehai Chen | Yang Xiang | Tiejun Zhao | Min Zhang
Findings of the Association for Computational Linguistics: EMNLP 2023

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.


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JCapsR: 一种联合胶囊神经网络的藏语知识图谱表示学习模型(JCapsR: A Joint Capsule Neural Network for Tibetan Knowledge Graph Representation Learning)
Yuan Sun (孙媛) | Jiaya Liang (梁家亚) | Andong Chen (陈安东) | Xiaobing Zhao (赵小兵)
Proceedings of the 20th Chinese National Conference on Computational Linguistics