Before revision:  We contribute a unified codebase and conduct extensive evaluations for baselines and state-of-the-arts: a blind GRU obtains 31.58 \\%DIF < , the best model achieves only 35.15\\%, and humans top at 97.5\\%, which offers great research potential to fill the gap.abstract %DIF > answering accuracy, which is even close to the human expert performance...
After revision:  Besides the QA task, KQA Pro can also serves for the semantic parsing task. As far as we know, it is currently the largest corpus of NLQ-to-SPARQL and NLQ-to-Program. We conduct extensive experiments to evaluate whether machines can learn to answer our complex questions in different cases, that is, with only QA supervision or with intermediate SPARQL/Program supervision. We find that state-of-the-art KBQA methods learnt from only QA pairs perform very poor on our dataset, implying our questions are more challenging than previous datasets. However, pretrained models learnt from our NLQ-to-SPARQL and NLQ-to-Program annotations surprisingly achieve about 90 \\%DIF < , the best model achieves only 35.15\\%, and humans top at 97.5\\%, which offers great research potential to fill the gap.abstract %DIF > answering accuracy, which is even close to the human expert performance...