论文标题
公寓：中国人使用平坦的变压器
FLAT: Chinese NER Using Flat-Lattice Transformer
论文作者
论文摘要
最近，通过合并信息，角色词晶格结构已被证明对中国命名实体识别（NER）是有效的。但是，由于晶格结构是复杂且动态的，因此大多数现有的基于晶格的模型都难以充分利用GPU的并行计算，并且通常具有低推理速度。在本文中，我们提出了平坦的：中国NER的平坦晶格变压器，该变压器将晶格结构转换为由跨度组成的平坦结构。每个跨度对应于字符或潜在单词及其在原始晶格中的位置。凭借变压器和精心设计的位置编码的力量，Flat可以充分利用晶格信息，并具有出色的并行化能力。四个数据集的实验表明，在性能和效率方面表现出均优于其他基于词典的模型。
Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, since the lattice structure is complex and dynamic, most existing lattice-based models are hard to fully utilize the parallel computation of GPUs and usually have a low inference-speed. In this paper, we propose FLAT: Flat-LAttice Transformer for Chinese NER, which converts the lattice structure into a flat structure consisting of spans. Each span corresponds to a character or latent word and its position in the original lattice. With the power of Transformer and well-designed position encoding, FLAT can fully leverage the lattice information and has an excellent parallelization ability. Experiments on four datasets show FLAT outperforms other lexicon-based models in performance and efficiency.
