Integrating Deep Learning with Logic Fusion for Information Extraction
Table 3: Comparison with baselines on relation extraction using ACE2005 dataset.
['[EMPTY]', '(Li and Ji 14)', '(Miwa and Bansal 16)', '(Zhang et al. 17)', '(Sun et al. 18)', '(Sun et al. 18) [ITALIC] G', 'Pipeline', 'Pipeline+feat', 'TransF', 'TransF [ITALIC] G', 'Ours', 'Ours [ITALIC] G']
Note that \citeauthorsun18 \shortcitesun18 used a non-decomposable global loss with normal local loss to jointly train entity and relation extraction. We denote by (Sun et al. 18) and (Sun et al. 18)G the model without and with the global loss, respectively. We also implemented the global loss as a fine-tuning step in our method, denoted by the subscript G. It is observed that TransF achieves the best result for relation extraction and comparable result for entity extraction without sophisticated global training strategy. Global loss does not provide clear benefit for our model. This might indicate that our model already explores global interactions. (Sun et al. 18)G requires substantial pretraining processes (larger than 500 epochs in our implementation) using the normal loss. As a comparison, our model is more convenient to implement and provides faster convergence, as will be shown later. The logic rules only show slight improvement. We deduce the reason to be the characteristics of the data. Indeed, there are very few absolute relationships between entity types and relations. In most cases, two related entity types can have multiple relation categories, making explicit rules less useful.