update citation

README.md

@@ -134,16 +134,26 @@ The CANNOT dataset is released under [CC BY-SA
 </a>
 
 ### Citation 
-Please cite our [INLG 2023 paper](https://arxiv.org/abs/2307.13989), if you use our dataset. 
+Please cite our [INLG 2023 paper](https://aclanthology.org/2023.inlg-main.12/), if you use our dataset. 
 **BibTeX:**
 ```bibtex
-@misc{anschütz2023correct,
-      title={This is not correct! Negation-aware Evaluation of Language Generation Systems}, 
-      author={Miriam Anschütz and Diego Miguel Lozano and Georg Groh},
-      year={2023},
-      eprint={2307.13989},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
+@inproceedings{anschutz-etal-2023-correct,
+    title = "This is not correct! Negation-aware Evaluation of Language Generation Systems",
+    author = {Ansch{\"u}tz, Miriam  and
+      Miguel Lozano, Diego  and
+      Groh, Georg},
+    editor = "Keet, C. Maria  and
+      Lee, Hung-Yi  and
+      Zarrie{\ss}, Sina",
+    booktitle = "Proceedings of the 16th International Natural Language Generation Conference",
+    month = sep,
+    year = "2023",
+    address = "Prague, Czechia",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.inlg-main.12/",
+    doi = "10.18653/v1/2023.inlg-main.12",
+    pages = "163--175",
+    abstract = "Large language models underestimate the impact of negations on how much they change the meaning of a sentence. Therefore, learned evaluation metrics based on these models are insensitive to negations. In this paper, we propose NegBLEURT, a negation-aware version of the BLEURT evaluation metric. For that, we designed a rule-based sentence negation tool and used it to create the CANNOT negation evaluation dataset. Based on this dataset, we fine-tuned a sentence transformer and an evaluation metric to improve their negation sensitivity. Evaluating these models on existing benchmarks shows that our fine-tuned models outperform existing metrics on the negated sentences by far while preserving their base models' performances on other perturbations."
 }
 ```