Lotus at WojoodNER Shared Task: Multilingual Transformers: Unveiling Flat and Nested Entity Recognition

Jiyong Li, Dilshod Azizov, Hilal AlQuabeh, Shangsong Liang

Abstract: We introduce our systems developed for two subtasks in the shared task “Wojood” on Arabic NER detection, part of ArabicNLP 2023. For Subtask 1, we employ the XLM-R model to predict Flat NER labels for given tokens using a single classifier capable of categorizing all labels. For Subtask 2, we use the XLM-R encoder by building 21 individual classifiers. Each classifier corresponds to a specific label and is designed to determine the presence of its respective label. In terms of performance, our systems achieved competitive micro-F1 scores of 0.83 for Subtask 1 and 0.76 for Subtask 2, according to the leaderboard scores.