Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task
This work addresses multilingual and low-resource NER challenges for NLP practitioners, but it is incremental as it builds on existing methods for specific competition tasks.
The paper tackles multilingual named entity recognition (NER) by proposing a unified framework that achieved third place in the Multilingual Track, fourth in the Code-Mixed Track, and seventh in the Chinese Track at SemEval 2022, with macro-F1 scores of 77.66, 84.35, and 74.00 respectively.
This paper describes our system, which placed third in the Multilingual Track (subtask 11), fourth in the Code-Mixed Track (subtask 12), and seventh in the Chinese Track (subtask 9) in the SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition. Our system's key contributions are as follows: 1) For multilingual NER tasks, we offer an unified framework with which one can easily execute single-language or multilingual NER tasks, 2) for low-resource code-mixed NER task, one can easily enhance his or her dataset through implementing several simple data augmentation methods and 3) for Chinese tasks, we propose a model that can capture Chinese lexical semantic, lexical border, and lexical graph structural information. Finally, our system achieves macro-f1 scores of 77.66, 84.35, and 74.00 on subtasks 11, 12, and 9, respectively, during the testing phase.