Reasoning over Hybrid Chain for Table-and-Text Open Domain QA
This work addresses the challenge of reasoning across tables and text for open-domain QA, offering improved performance and interpretability, though it is incremental in advancing existing methods.
The paper tackles the problem of question answering over heterogeneous table-and-text data by proposing a ChAin-centric Reasoning and Pre-training framework (CARP), which achieves state-of-the-art performance on the OTT-QA benchmark.
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.