Update README.md

README.md

@@ -17,10 +17,21 @@ This model is continuously pre-trained from a `roberta-base` checkpoint on ~3.2M
 If you use this model, please cite the following paper:
 
 ```
-@article{zhang2024nnose,
-  title={NNOSE: Nearest Neighbor Occupational Skill Extraction},
-  author={Zhang, Mike and van der Goot, Rob and Kan, Min-Yen and Plank, Barbara},
-  journal={arXiv preprint arXiv:2401.17092},
-  year={2024}
+@inproceedings{zhang-etal-2024-nnose,
+    title = "{NNOSE}: Nearest Neighbor Occupational Skill Extraction",
+    author = "Zhang, Mike  and
+      Goot, Rob  and
+      Kan, Min-Yen  and
+      Plank, Barbara",
+    editor = "Graham, Yvette  and
+      Purver, Matthew",
+    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = mar,
+    year = "2024",
+    address = "St. Julian{'}s, Malta",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.eacl-long.35",
+    pages = "589--608",
+    abstract = "The labor market is changing rapidly, prompting increased interest in the automatic extraction of occupational skills from text. With the advent of English benchmark job description datasets, there is a need for systems that handle their diversity well. We tackle the complexity in occupational skill datasets tasks{---}combining and leveraging multiple datasets for skill extraction, to identify rarely observed skills within a dataset, and overcoming the scarcity of skills across datasets. In particular, we investigate the retrieval-augmentation of language models, employing an external datastore for retrieving similar skills in a dataset-unifying manner. Our proposed method, \textbf{N}earest \textbf{N}eighbor \textbf{O}ccupational \textbf{S}kill \textbf{E}xtraction (NNOSE) effectively leverages multiple datasets by retrieving neighboring skills from other datasets in the datastore. This improves skill extraction \textit{without} additional fine-tuning. Crucially, we observe a performance gain in predicting infrequent patterns, with substantial gains of up to 30{\%} span-F1 in cross-dataset settings.",
 }
 ```