Update README.md

README.md

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 ---
 
 # gaELECTRA
-[gaELECTRA](https://arxiv.org/abs/2107.12930) is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.
+[gaELECTRA](https://aclanthology.org/2022.lrec-1.511/) is an ELECTRA model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper. For fine-tuning this model on a token classification task, e.g. Named Entity Recognition, use the discriminator model.
 
 ### Limitations and bias
 Some data used to pretrain gaBERT was scraped from the web which potentially contains ethically problematic text (bias, hate, adult content, etc.). Consequently, downstream tasks/applications using gaBERT should be thoroughly tested with respect to ethical considerations.
@@ -20,24 +20,23 @@ Some data used to pretrain gaBERT was scraped from the web which potentially con
 If you use this model in your research, please consider citing our paper:
 
 ```
-@article{DBLP:journals/corr/abs-2107-12930,
-  author    = {James Barry and
-               Joachim Wagner and
-               Lauren Cassidy and
-               Alan Cowap and
-               Teresa Lynn and
-               Abigail Walsh and
-               M{\'{\i}}che{\'{a}}l J. {\'{O}} Meachair and
-               Jennifer Foster},
-  title     = {gaBERT - an Irish Language Model},
-  journal   = {CoRR},
-  volume    = {abs/2107.12930},
-  year      = {2021},
-  url       = {https://arxiv.org/abs/2107.12930},
-  archivePrefix = {arXiv},
-  eprint    = {2107.12930},
-  timestamp = {Fri, 30 Jul 2021 13:03:06 +0200},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-2107-12930.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
+@inproceedings{barry-etal-2022-gabert,
+    title = "ga{BERT} {---} an {I}rish Language Model",
+    author = "Barry, James  and
+      Wagner, Joachim  and
+      Cassidy, Lauren  and
+      Cowap, Alan  and
+      Lynn, Teresa  and
+      Walsh, Abigail  and
+      {\'O} Meachair, M{\'\i}che{\'a}l J.  and
+      Foster, Jennifer",
+    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
+    month = jun,
+    year = "2022",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2022.lrec-1.511",
+    pages = "4774--4788",
+    abstract = "The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.",
 }
 ```