Update README.md

README.md

@@ -128,16 +128,33 @@ The images in YesBut Dataset do not include any personal identifiable informatio
 
 **BibTeX:**
 
-@article{nandy2024yesbut,
-  title={YesBut: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models},
-  author={Nandy, Abhilash and Agarwal, Yash and Patwa, Ashish and Das, Millon Madhur and Bansal, Aman and Raj, Ankit and Goyal, Pawan and Ganguly, Niloy},
-  journal={arXiv preprint arXiv:2409.13592},
-  year={2024}
+@inproceedings{nandy-etal-2024-yesbut,
+    title = "***{Y}es{B}ut***: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models",
+    author = "Nandy, Abhilash  and
+      Agarwal, Yash  and
+      Patwa, Ashish  and
+      Das, Millon Madhur  and
+      Bansal, Aman  and
+      Raj, Ankit  and
+      Goyal, Pawan  and
+      Ganguly, Niloy",
+    editor = "Al-Onaizan, Yaser  and
+      Bansal, Mohit  and
+      Chen, Yun-Nung",
+    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2024",
+    address = "Miami, Florida, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.emnlp-main.937",
+    doi = "10.18653/v1/2024.emnlp-main.937",
+    pages = "16878--16895",
+    abstract = "Understanding satire and humor is a challenging task for even current Vision-Language models. In this paper, we propose the challenging tasks of Satirical Image Detection (detecting whether an image is satirical), Understanding (generating the reason behind the image being satirical), and Completion (given one half of the image, selecting the other half from 2 given options, such that the complete image is satirical) and release a high-quality dataset ***YesBut***, consisting of 2547 images, 1084 satirical and 1463 non-satirical, containing different artistic styles, to evaluate those tasks. Each satirical image in the dataset depicts a normal scenario, along with a conflicting scenario which is funny or ironic. Despite the success of current Vision-Language Models on multimodal tasks such as Visual QA and Image Captioning, our benchmarking experiments show that such models perform poorly on the proposed tasks on the ***YesBut*** Dataset in Zero-Shot Settings w.r.t both automated as well as human evaluation. Additionally, we release a dataset of 119 real, satirical photographs for further research.",
 }
 
-**APA:**
+**ACL:**
 
-Nandy, A., Agarwal, Y., Patwa, A., Das, M. M., Bansal, A., Raj, A., ... & Ganguly, N. (2024). YesBut: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models. arXiv preprint arXiv:2409.13592.
+Abhilash Nandy, Yash Agarwal, Ashish Patwa, Millon Madhur Das, Aman Bansal, Ankit Raj, Pawan Goyal, and Niloy Ganguly. 2024. ***YesBut***: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 16878–16895, Miami, Florida, USA. Association for Computational Linguistics.
 
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