Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA
Junjie Huang, Wanjun Zhong, Qian Liu, Ming Gong, Daxin Jiang, Nan Duan · 2022
Retrieving evidences from tabular and textual resources is essential for open-domain question answering (OpenQA), which provides more comprehensive information.However, training an effective dense table-text retriever is difficult due to the challenges of table-text discrepancy and data sparsity problem.To address the above challenges, we introduce an optimized OpenQA Table-TExt Retriever (OTTER) to jointly retrieve tabular and textual evidences.Firstly, we propose to enhance mixed-modality representation learning via two mechanisms: modality-enhanced representation and mixedmodality negative sampling strategy.Secondly, to alleviate data sparsity problem and enhance the general retrieval ability, we conduct retrieval-centric mixed-modality synthetic pre-training.Experimental results demonstrate that OTTER substantially improves the performance of table-and-text retrieval on the OTT-QA dataset.Comprehensive analyses examine the effectiveness of all the proposed mechanisms.Besides, equipped with OTTER, our OpenQA system achieves the state-of-the-art result on the downstream QA task, with 10.1% absolute performance gain in terms of the exact match over the previous best system. 1