Integrating Deep Learning with Logic Fusion for Information Extraction
Table 1: Comparison with baselines on OTE.
['[EMPTY]', 'Restaurant14', 'Laptop14', 'Restaurant16']
Comparison on OTE task: The last two rows indicate our proposed models, where TransF is the deep learning module without logic integration. Since the OTE task can be viewed as single-class entity extraction, the proposed model can be adapted to this task by ignoring relation predictions. From the results, we can observe that even without logic rules, the transformer model is able to achieve 2-out-of-3 best results compared to existing works. This proves the effectiveness of transformer for implicit interaction modeling. When considering logic knowledge, we use segmentation rules that enforces possible segmentation labels for 2 adjacent words. Furthermore, we also incorporate implicit relational rules that state entity(X)∧entity(Z)⇒related(X,Z). This is achieved by keeping the relation prediction layer in the deep learning module. We find this strategy slightly improves our results which will be shown later. Although \citeauthorYu19 \shortciteYu19 incorporated explicit constraints through integer linear programming, the separation from DNN during learning makes it suboptimal. This demonstrates the advantage of our unified framework that associates logic reasoning with representation learning. Clearly, our model achieves the state-of-the-art results on all 3 datasets.