In this paper, we propose an approach for transferring the knowledge of a
neural model for sequence labeling, learned from the source domain, to a new
model trained on a target domain, where new label categories appear. Our
transfer learning (TL) techniques enable to adapt the source model using the
target data and new categories, without accessing to the source data. Our
solution consists in adding new neurons in the output layer of the target model
and transferring parameters from the source model, which are then fine-tuned
with the target data. Additionally, we propose a neural adapter to learn the
difference between the source and the target label distribution, which provides
additional important information to the target model. Our experiments on Named
Entity Recognition show that (i) the learned knowledge in the source model can
be effectively transferred when the target data contains new categories and
(ii) our neural adapter further improves such transfer.

本文提出一种基于迁移学习的方法，通过在目标模型输出层添加新的神经元并转移源模型参数，实现对源模型知识的有效传递，且通过神经适配器进一步改善了命名实体识别任务中的标签迁移。