Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task

TL;DR

This paper presents a unified multilingual NER framework that improves performance across various languages and tasks, incorporating data augmentation and specialized Chinese language modeling, achieving competitive results in SemEval 2022.

Contribution

The paper introduces a versatile framework for multilingual NER, enhances low-resource code-mixed NER with data augmentation, and develops a Chinese-specific model capturing lexical semantics and structure.

Findings

Achieved macro-f1 scores of 77.66, 84.35, and 74.00 on three subtasks.

Placed third, fourth, and seventh in respective SemEval 2022 subtasks.

Demonstrated effectiveness of data augmentation and language-specific modeling.

Abstract

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.