Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
Table 3: AUC values of different models.
['[BOLD] No.', '[BOLD] Model', '[BOLD] AUC']
Eight models were implemented for comparison. At the training stage of the ATT_BL method, the relation query vector for attention weight calculation was fixed as the embedding vector associated with the distant supervision label for each bag. At the test stage, all relation query vectors were applied to calculate the posterior probabilities of relations respectively and the relation with the highest probability was chosen as the classification result (1) Similar to the results of previous work Zeng et al. (2) When using either CNN or PCNN sentence encoders, ATT_RA outperformed ATT_BL. It can be attributed to that the ATT_BL method only considered the target relation when deriving bag representations at training time, while the ATT_RA method calculated intra-bag attention weights using all relation embeddings as queries, which improved the flexibility of bag representations. (3) For both sentence encoders and both intra-bag attention methods, the models with BAG_ATT always achieved better performances than the ones without BAG_ATT. This result verified the effectiveness of our proposed inter-bag attention method for distant supervision relation extraction. (4) The best AUC performance was achieved by combining PCNN sentence encoders with the intra-bag and inter-bag attentions proposed in this paper.