  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 mixed-modality 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
improvement in terms of the exact match over the previous best system.
  All the code and data are available at
https://github.com/Jun-jie-Huang/OTTeR.
