Update README.md

README.md

@@ -130,16 +130,30 @@ SentenceTransformer(
 **Bibtex**: 
 
 
-@inproceedings{Hossain2024SemanticCUETSync,
-  author    = {Hossain, Md. Sajjad and Paran, Ashraful Islam and Shohan, Symom Hossain and Hossain, Jawad and Hoque, Mohammed Moshiul},
-  title     = {SemanticCUETSync at SemEval-2024 Task 1: Finetuning Sentence Transformer to Find Semantic Textual Relatedness},
-  booktitle = {Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)},
-  year      = {2024},
-  pages     = {1212--1218},
-  address   = {Mexico City, Mexico},
-  publisher = {Association for Computational Linguistics},
+@inproceedings{hossain-etal-2024-semanticcuetsync,
+    title = "{S}emantic{CUETS}ync at {S}em{E}val-2024 Task 1: Finetuning Sentence Transformer to Find Semantic Textual Relatedness",
+    author = "Hossain, Md. Sajjad  and
+      Paran, Ashraful Islam  and
+      Shohan, Symom Hossain  and
+      Hossain, Jawad  and
+      Hoque, Mohammed Moshiul",
+    editor = {Ojha, Atul Kr.  and
+      Do{\u{g}}ru{\"o}z, A. Seza  and
+      Tayyar Madabushi, Harish  and
+      Da San Martino, Giovanni  and
+      Rosenthal, Sara  and
+      Ros{\'a}, Aiala},
+    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
+    month = jun,
+    year = "2024",
+    address = "Mexico City, Mexico",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.semeval-1.178",
+    pages = "1222--1228",
+    abstract = "Semantic textual relatedness is crucial to Natural Language Processing (NLP). Methodologies often exhibit superior performance in high-resource languages such as English compared to low-resource ones like Marathi, Telugu, and Spanish. This study leverages various machine learning (ML) approaches, including Support Vector Regression (SVR) and Random Forest, deep learning (DL) techniques such as Siamese Neural Networks, and transformer-based models such as MiniLM-L6-v2, Marathi-sbert, Telugu-sentence-bert-nli, and Roberta-bne-sentiment-analysis-es, to assess semantic relatedness across English, Marathi, Telugu, and Spanish. The developed transformer-based methods notably outperformed other models in determining semantic textual relatedness across these languages, achieving a Spearman correlation coefficient of 0.822 (for English), 0.870 (for Marathi), 0.820 (for Telugu), and 0.677 (for Spanish). These results led to our work attaining rankings of 22th (for English), 11th (for Marathi), 11th (for Telegu) and 14th (for Spanish), respectively.",
 }
 
+
 **Authors**: