KenSwQuAD -- A Question Answering Dataset for Swahili Low Resource Language

Wanjawa, Barack Wamkaya; Wanzare, Lilian D. A.; Indede, Florence; McOnyango, Owen; Muchemi, Lawrence; Ombui, Edward

doi:10.48550/arxiv.2205.02364

Cited by 1 publication

(1 citation statement)

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“…These problems largely arise from data scarcity and lack of domain-specific training, which often results in underfitting. Even though we are now in the era of large models, which have considerably advanced the field of natural language processing, these low-resource problems in QA tasks persist (Wanjawa et al, 2022;Sun et al, 2021b;Chen et al, 2023a). Consequently, addressing these lowresource issues has become an essential research focus, with data augmentation strategies emerging as a common and effective approach.…”

Section: Introductionmentioning

confidence: 99%

Improving Low-resource Question Answering by Augmenting Question Information

Chen,

Sun,

Zhao

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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In the era of large models, low-resource question-answering tasks lag, emphasizing the importance of data augmentation. 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 1 .

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Section: Introductionmentioning

confidence: 99%