Improving the Efficiency and Effectiveness for BERT-based Entity Resolution

BERT has set a new state-of-the-art performance on entity resolution (ER) task, largely owed to fine-tuning pretrained language models and the deep pair-wise interaction. Albeit being remarkably effective, it comes with a steep increase in computational cost, as the deep-interaction requires to exhaustively compute every tuple pair to search for coreferences. For ER task, it is often prohibitively expensive due to the large cardinality to be matched. To tackle this, we introduce a siamese network structure that independently encodes tuples using BERT but delays the pair-wise interaction via an enhanced alignment network. This siamese structure enables a dedicated blocking module to quickly filter out obviously dissimilar tuple pairs, and thus drastically reduces the cardinality of fine-grained matching. Further, the blocking and entity matching are integrated into a multi-task learning framework for facilitating both tasks. Extensive experiments on multiple datasets demonstrate that our model significantly outperforms state-of-the-art models (including BERT) in both efficiency and effectiveness.