Table Question Answering for Low-resourced Indic Languages
Vaishali Pal, Evangelos Kanoulas, Andrew Yates, Maarten de Rijke · 2024
TableQA is the task of answering questions over tables of structured information, returning individual cells or tables as output.TableQA research has focused primarily on high-resource languages, leaving medium-and low-resource languages with little progress due to scarcity of annotated data and neural models.We address this gap by introducing a fully automatic large-scale table question answering (tableQA) data generation process for low-resource languages with limited budget.We incorporate our data generation method on two Indic languages, Bengali and Hindi, which have no tableQA datasets or models.TableQA models trained on our large-scale datasets outperform stateof-the-art LLMs.We further study the trained models on different aspects, including mathematical reasoning capabilities and zero-shot cross-lingual transfer.Our work is the first on low-resource tableQA focusing on scalable data generation and evaluation procedures.Our proposed data generation method can be applied to any low-resource language with a web presence.We release datasets, models, and code.