Guessing What’s Plausible But Remembering What’s True: Accurate Neural Reasoning for Question-Answering
Table 5: Hits@1 of WebQuestionsSP and MetaQA (2-hop and 3-hop) datasets. (We re-run GRAFT-Net and PullNet on WebQuestionsSP with oracle entities.)
['[EMPTY]', 'MetaQA2', 'MetaQA3', 'WebQSP']
5.2.3 Results We achieved a new state-of-the-art on the MetaQA 3-hop and WebQuestionSP datasets, beating the previous state-of-the-art by a large margin (7.7% and 5.8% hits@1 absolute improvement). The results on the MetaQA 2-hop dataset are comparable to the previous state-of-the-art. We note that unlike PullNet, EmQL does not require any intermediate supervision—it learns only from the final answers.