Samsung Research China - Beijing at SemEval-2023 Task 2: An AL-R Model for Multilingual Complex Named Entity Recognition
Haojie Zhang, Xiao Li, Renhua Gu, Xiaoyan Qu, Xiangfeng Meng, Shuo Hu, Song Liu · 2023
This paper describes our system for SemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II).Our team Samsung Research China -Beijing proposes an AL-R (Adjustable Loss RoBERTa) model to boost the performance of recognizing short and complex entities with the challenges of longtail data distribution, out of knowledge base and noise scenarios.We first employ an adjustable dice loss optimization objective to overcome the issue of long-tail data distribution, which is also proved to be noise-robusted, especially in combatting the issue of fine-grained label confusing.Besides, we develop our own knowledge enhancement tool to provide related contexts for the short context setting and address the issue of out of knowledge base.Experiments have verified the validation of our approaches.In the official test result, our system ranked 2nd on the English track in this task.