HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data
Table 5: Experimental results of different models, In-Table refers to the subset of questions which have their answers in the table, In-Passage refers to the subset of questions which have their answer in a certain passage.
['Model', 'Dev In-Table', 'Dev In-Passage', 'Dev Total', 'Test In-Table', 'Test In-Passage', 'Test Total']
The In-Table questions are remarkably simpler than In-Passage question because they do not the RC reasoning step, the overall accuracy is roughly 8-10% higher than its counterpart. With the experimented model variants, the best accuracy is achieved with BERT-large-uncased as backend, which can beat the BERT-base-uncased by roughly 2%. However, its performance is still far lagged behind human performance, leaving ample room for the future research.