Shuo Hu


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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
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This paper describes our system for SemEval-2023 Task 2 Multilingual Complex Named EntityRecognition (MultiCoNER II). Our teamSamsung Research China - Beijing proposesan AL-R (Adjustable Loss RoBERTa) model toboost the performance of recognizing short andcomplex entities with the challenges of longtaildata distribution, out of knowledge base andnoise scenarios. We first employ an adjustabledice loss optimization objective to overcomethe issue of long-tail data distribution, which isalso proved to be noise-robusted, especially incombatting the issue of fine-grained label confusing. Besides, we develop our own knowledgeenhancement tool to provide related contextsfor the short context setting and addressthe issue of out of knowledge base. Experimentshave verified the validation of our approaches.


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Cross-Lingual Topic Alignment in Time Series Japanese / Chinese News
Shuo Hu | Yusuke Takahashi | Liyi Zheng | Takehito Utsuro | Masaharu Yoshioka | Noriko Kando | Tomohiro Fukuhara | Hiroshi Nakagawa | Yoji Kiyota
Proceedings of the 26th Pacific Asia Conference on Language, Information, and Computation