Merge pull request #3 from StEvUgnIn/patch-1

Replace the former ACM citation by the current proceeding in ACL EMNLP conference

README.md

@@ -70,21 +70,30 @@ Coming soon!
 ## Citation
 Please cite the model with the following citation:
 ```bibtex
-@INPROCEEDINGS{shah-etal-2022-flang,
-    author = {Shah, Raj Sanjay  and
-      Chawla, Kunal and
-      Eidnani, Dheeraj and
-      Shah, Agam and
-      Du, Wendi and
-      Chava, Sudheer and
-      Raman, Natraj and
-      Smiley, Charese and
-      Chen, Jiaao and
-      Yang, Diyi },
-    title = {When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain},
-    booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
-    year = {2022},
-    publisher = {Association for Computational Linguistics}
+@inproceedings{shah-etal-2022-flue,
+    title = "When {FLUE} Meets {FLANG}: Benchmarks and Large Pretrained Language Model for Financial Domain",
+    author = "Shah, Raj  and
+      Chawla, Kunal  and
+      Eidnani, Dheeraj  and
+      Shah, Agam  and
+      Du, Wendi  and
+      Chava, Sudheer  and
+      Raman, Natraj  and
+      Smiley, Charese  and
+      Chen, Jiaao  and
+      Yang, Diyi",
+    editor = "Goldberg, Yoav  and
+      Kozareva, Zornitsa  and
+      Zhang, Yue",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, United Arab Emirates",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.emnlp-main.148",
+    doi = "10.18653/v1/2022.emnlp-main.148",
+    pages = "2322--2335",
+    abstract = "Pre-trained language models have shown impressive performance on a variety of tasks and domains. Previous research on financial language models usually employs a generic training scheme to train standard model architectures, without completely leveraging the richness of the financial data. We propose a novel domain specific Financial LANGuage model (FLANG) which uses financial keywords and phrases for better masking, together with span boundary objective and in-filing objective. Additionally, the evaluation benchmarks in the field have been limited. To this end, we contribute the Financial Language Understanding Evaluation (FLUE), an open-source comprehensive suite of benchmarks for the financial domain. These include new benchmarks across 5 NLP tasks in financial domain as well as common benchmarks used in the previous research. Experiments on these benchmarks suggest that our model outperforms those in prior literature on a variety of NLP tasks. Our models, code and benchmark data will be made publicly available on Github and Huggingface.",
 }
 ```