# LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER using XLM-RoBERTa

arXiv: https://arxiv.org/abs/2305.03300 · HTML: https://mlsanity.com/paper/2305.03300/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 3 of 3)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)

## Built on by (most cited first, 7 of 7)

- [SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)](https://mlsanity.com/paper/2305.06586/) (2023)
- [Low-Resource Machine Translation through Retrieval-Augmented LLM Prompting: A Study on the Mambai Language](https://mlsanity.com/paper/2404.04809/) (2024)
- [Decoding the Diversity: A Review of the Indic AI Research Landscape](https://mlsanity.com/paper/2406.09559/) (2024)
- [The Grammar and Syntax Based Corpus Analysis Tool For The Ukrainian Language](https://mlsanity.com/paper/2305.13530/) (2023)
- [FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual Framework](https://mlsanity.com/paper/2502.02391/) (2025)
- [Hierarchical Indexing with Knowledge Enrichment for Multilingual Video Corpus Retrieval](https://mlsanity.com/paper/2510.09553/) (2025)
- [Hidden Persuasion: Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasion of Ukraine](https://mlsanity.com/paper/2505.24028/) (2025)

## Abstract (arXiv)

Named Entity Recognition(NER) is a task of recognizing entities at a token level in a sentence. This paper focuses on solving NER tasks in a multilingual setting for complex named entities. Our team, LLM-RM participated in the recently organized SemEval 2023 task, Task 2: MultiCoNER II,Multilingual Complex Named Entity Recognition. We approach the problem by leveraging cross-lingual representation provided by fine-tuning XLM-Roberta base model on datasets of all of the 12 languages provided -- Bangla, Chinese, English, Farsi, French, German, Hindi, Italian, Portuguese, Spanish, Swedish and Ukrainian