  Tabular and textual question answering requires systems to perform reasoning
over heterogeneous information, considering table structure, and the
connections among table and text. In this paper, we propose a ChAin-centric
Reasoning and Pre-training framework (CARP). CARP utilizes hybrid chain to
model the explicit intermediate reasoning process across table and text for
question answering. We also propose a novel chain-centric pre-training method,
to enhance the pre-trained model in identifying the cross-modality reasoning
process and alleviating the data sparsity problem. This method constructs the
large-scale reasoning corpus by synthesizing pseudo heterogeneous reasoning
paths from Wikipedia and generating corresponding questions. We evaluate our
system on OTT-QA, a large-scale table-and-text open-domain question answering
benchmark, and our system achieves the state-of-the-art performance. Further
analyses illustrate that the explicit hybrid chain offers substantial
performance improvement and interpretablity of the intermediate reasoning
process, and the chain-centric pre-training boosts the performance on the chain
extraction.
