Content-Based Table Retrieval for Web Queries
Table 4: Results on the WikiTableQuestions dataset with different features.
['Setting', 'MAP', 'P@1']
We implement two baselines. The first baseline is BM25, which is the same baseline we have used for comparison on the WebQueryTable dataset. The second baseline is header grounding, which is partly inspired by Venetis et al. We implement a CDSSM We train the model by minimizing the cross-entropy error, where the ground truth is the header of the answer. We can find that designed features perform comparably with neural networks, and both of them perform better than BM25 and column grounding baselines. Combining designed features and neural networks obtains further improvement.