Answering Complicated Question Intents Expressed in Decomposed Question Sequences
Table 1: Accuracy of existing systems on our datasets on all questions and questions at all positions within the sequence.
['Model', 'All', 'Pos 1', 'Pos 2', 'Pos 3', 'Pos 4']
We present both the overall accuracy and the accuracy of answers to questions at each position. Although the accuracy of fp on position-1 questions (48.7%) is much higher than its performance on WTQ (37.0%), the overall accuracy (32.8%) is still lower, which indicates that our SQA dataset remains difficult. In addition, the neural model significantly underperforms fp, suggesting that it requires more data or more sophisticated architectural design to generalize to all of SQA’s complexities. As we observed in Sec. By leveraging fp we propose two ways to deal with this issue: question rewriting and table rewriting. When checking these questions in detail, we find that the majority of the errors are due to the semantic matching problem – mismatches between question text and table text.