Update README.md

README.md

@@ -93,4 +93,21 @@ sports
 
 ## BibTeX entry and citation info
 
-TBA
+@inproceedings{antypas-etal-2024-multilingual,
+    title = "Multilingual Topic Classification in {X}: Dataset and Analysis",
+    author = "Antypas, Dimosthenis  and
+      Ushio, Asahi  and
+      Barbieri, Francesco  and
+      Camacho-Collados, Jose",
+    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.1123",
+    pages = "20136--20152",
+    abstract = "In the dynamic realm of social media, diverse topics are discussed daily, transcending linguistic boundaries. However, the complexities of understanding and categorising this content across various languages remain an important challenge with traditional techniques like topic modelling often struggling to accommodate this multilingual diversity. In this paper, we introduce X-Topic, a multilingual dataset featuring content in four distinct languages (English, Spanish, Japanese, and Greek), crafted for the purpose of tweet topic classification. Our dataset includes a wide range of topics, tailored for social media content, making it a valuable resource for scientists and professionals working on cross-linguistic analysis, the development of robust multilingual models, and computational scientists studying online dialogue. Finally, we leverage X-Topic to perform a comprehensive cross-linguistic and multilingual analysis, and compare the capabilities of current general- and domain-specific language models.",
+}