Update README.md

README.md

@@ -11,12 +11,21 @@ Mike Zhang, Rob van der Goot, and Barbara Plank. In ACL (2023).
 If you use this work please cite the following (for now arXiv):
 
 ```
-@article{zhang2023escoxlm,
-  title={ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain},
-  author={Zhang, Mike and van der Goot, Rob and Plank, Barbara},
-  journal={arXiv preprint arXiv:2305.12092},
-  year={2023}
+@inproceedings{zhang-etal-2023-escoxlm,
+    title = "{ESCOXLM}-{R}: Multilingual Taxonomy-driven Pre-training for the Job Market Domain",
+    author = "Zhang, Mike  and
+      van der Goot, Rob  and
+      Plank, Barbara",
+    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.acl-long.662",
+    pages = "11871--11890",
+    abstract = "The increasing number of benchmarks for Natural Language Processing (NLP) tasks in the computational job market domain highlights the demand for methods that can handle job-related tasks such as skill extraction, skill classification, job title classification, and de-identification. While some approaches have been developed that are specific to the job market domain, there is a lack of generalized, multilingual models and benchmarks for these tasks. In this study, we introduce a language model called ESCOXLM-R, based on XLM-R-large, which uses domain-adaptive pre-training on the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy, covering 27 languages. The pre-training objectives for ESCOXLM-R include dynamic masked language modeling and a novel additional objective for inducing multilingual taxonomical ESCO relations. We comprehensively evaluate the performance of ESCOXLM-R on 6 sequence labeling and 3 classification tasks in 4 languages and find that it achieves state-of-the-art results on 6 out of 9 datasets. Our analysis reveals that ESCOXLM-R performs better on short spans and outperforms XLM-R-large on entity-level and surface-level span-F1, likely due to ESCO containing short skill and occupation titles, and encoding information on the entity-level.",
 }
+
 ```
 
 Find more information in the Github repository: https://github.com/jjzha/escoxlmr