Update README.md

README.md

@@ -30,23 +30,26 @@ The model was evaluated in [our paper](https://arxiv.org/abs/2109.15254) [Pikuli
 ## Cite
 
 ```
-@article{DBLP:journals/corr/abs-2109-15254,
-  author    = {Mat{\'{u}}{\v{s}} Pikuliak and
-               {\v{S}}tefan Grivalsk{\'{y}} and
-               Martin Kon{\^{o}}pka and
-               Miroslav Bl{\v{s}}t{\'{a}}k and
-               Martin Tamajka and
-               Viktor Bachrat{\'{y}} and
-               Mari{\'{a}}n {\v{S}}imko and
-               Pavol Bal{\'{a}}{\v{z}}ik and
-               Michal Trnka and
-               Filip Uhl{\'{a}}rik},
-  title     = {SlovakBERT: Slovak Masked Language Model},
-  journal   = {CoRR},
-  volume    = {abs/2109.15254},
-  year      = {2021},
-  url       = {https://arxiv.org/abs/2109.15254},
-  eprinttype = {arXiv},
-  eprint    = {2109.15254},
+@inproceedings{pikuliak-etal-2022-slovakbert,
+    title = "{S}lovak{BERT}: {S}lovak Masked Language Model",
+    author = "Pikuliak, Mat{\'u}{\v{s}}  and
+      Grivalsk{\'y}, {\v{S}}tefan  and
+      Kon{\^o}pka, Martin  and
+      Bl{\v{s}}t{\'a}k, Miroslav  and
+      Tamajka, Martin  and
+      Bachrat{\'y}, Viktor  and
+      Simko, Marian  and
+      Bal{\'a}{\v{z}}ik, Pavol  and
+      Trnka, Michal  and
+      Uhl{\'a}rik, Filip",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, United Arab Emirates",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.findings-emnlp.530",
+    pages = "7156--7168",
+    abstract = "We introduce a new Slovak masked language model called \textit{SlovakBERT}. This is to our best knowledge the first paper discussing Slovak transformers-based language models. We evaluate our model on several NLP tasks and achieve state-of-the-art results. This evaluation is likewise the first attempt to establish a benchmark for Slovak language models. We publish the masked language model, as well as the fine-tuned models for part-of-speech tagging, sentiment analysis and semantic textual similarity.",
 }
+
 ```