Dynamically Fused Graph Network for Multi-hop Reasoning
Table 1: Performance comparison on the private test set of HotpotQA in the distractor setting. Our DFGN is the second best result on the leaderboard before submission (on March 1st). The baseline model is from yang2018hotpotqa and the results with ∗ is unpublished. DFGN(Ours)† refers to the same model with a revised entity graph, whose entities are recognized by a BERT NER model. Note that the result of DFGN(Ours)† is submitted to the leaderboard during the review process of our paper.
['Model', 'Answer', 'Answer', 'Sup Fact', 'Sup Fact', 'Joint', 'Joint']
Besides, the answer performance and the joint performance of our model are competitive against state-of-the-art unpublished models. We also include the result of our model with a revised entity graph whose entities are recognized by a BERT NER model Devlin et al. We fine-tune the pre-trained BERT model on the dataset of the CoNLL’03 NER shared task Sang and De Meulder The results show that our model achieves a 1.5% gain in the joint F1-score with the entity graph built from a better entity recognizer.