A Discrete Hard EM Approach for Weakly Supervised Question Answering
Table 4: Results on WikiSQL. We compare accuracy with weakly-supervised or fully-supervised settings. Our method outperforms previous weakly-supervised methods and most of published fully-supervised methods.
['Model', 'Accuracy Dev', 'Accuracy Test']
These results indicate that precomputing a solution set and training a model through hard updates play a significant role to the performance. Given that our method does not require SQL executions at training time (unlike MAPO), it provides a simpler, more effective and time-efficient strategy. Comparing to previous models with full supervision, our results are still on par and outperform most of the published results.