grvkamath/low-resource-syn-ner

Code and data used in the paper "Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?", to be presented at AACL 2025 (Main Conference). # Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages? *Gaurav Kamath \& Sowmya Vajjala* This repository contains code and data for the paper "Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?", to be presented at AACL 2025 (Main Conference). This work was conducted as part of an internship Gaurav Kamath did at the National Research Council, Canada. ## Contents Below is the structure of this repository: * `scripts`: Scripts used for all steps of this study, including both data generation and model training. * `datasets`: Datasets used for this study. This folder includes synthetic, LLM-generated datasets, as well as the raw outputs from models when generating them. This folder also contains portions of the Universal NER dataset (Mayhew et al. 2023), which was not available on HuggingFace Datasets at the time this study began. The MasakhaNER 2 (Adelani et al. 2022) and Naamapadam (Mhaske et al. 2023) datasets can be found on HuggingFace Datasets. * `results`: All results files from our experiments. ### References Adelani, D. I., Neubig, G., Ruder, S., Rijhwani, S., Beukman, M., Palen-Michel, C., ... & Klakow, D. (2022, December). MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition. In *Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing* (pp. 4488-4508). Mayhew, S., Blevins, T., Liu, S., Šuppa, M., Gonen, H., Imperial, J. M., ... & Pinter, Y. (2024, June). Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark. In *Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)* (pp. 4322-4337). Mhaske, A., Kedia, H., Doddapaneni, S., Khapra, M. M., Kumar, P., Murthy, R., & Kunchukuttan, A. (2023, July). Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages. In *Proceedings of the 61st Annual Meeting …