MCL: Multi-Granularity Contrastive Learning Framework for Chinese NER

Abstract

Recently, researchers have applied the word-character lattice framework to integrated word information, which has become very popular for Chinese named entity recognition (NER). However, prior approaches fuse word information by different variants of encoders such as Lattice LSTM or Flat-Lattice Transformer, but are still not data-efficient indeed to fully grasp the depth interaction of cross-granularity and important word information from the lexicon. In this paper, we go beyond the typical lattice structure and propose a novel Multi-Granularity Contrastive Learning framework (MCL), that aims to optimize the inter-granularity distribution distance and emphasize the critical matched words in the lexicon. By carefully combining cross-granularity contrastive learning and bi-granularity contrastive learning, the network can explicitly leverage lexicon information on the initial lattice structure, and further provide more dense interactions of across-granularity, thus significantly improving model performance. Experiments on four Chinese NER datasets show that MCL obtains state-of-the-art results while considering model efficiency. The source code of the proposed method is publicly available at https://github.com/zs50910/MCL