Update README.md

README.md

@@ -19,3 +19,24 @@ The annotations were conducted on a sheet in the following **dissonance-first**
 The annotators used the following flowchart as a more detailed guide to determining the Dissonance, Consonance and Neither/Other classes: 
 
 ![annotation guidelines](./annotation_guidelines.jpg)
+
+## Citation
+
+If you use this dataset, please cite the associated paper:
+
+```
+
+@inproceedings{varadarajan2023transfer,
+    title={Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge},
+    author={Varadarajan, Vasudha and Juhng, Swanie and Mahwish, Syeda and Liu, Xiaoran and Luby, Jonah and Luhmann, Christian and Schwartz, H Andrew},
+    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Long Papers)",
+    month = july,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    abstract = "While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks -- when the class label is very infrequent (e.g. < 5% of samples). Active learning has in general been proposed to alleviate such challenges, but choice of selection strategy, the criteria by which rare-class examples are chosen, has not been systematically evaluated. Further, transformers enable iterative transfer-learning approaches. We propose and investigate transfer- and active learning solutions to the rare class problem of dissonance detection through utilizing models trained on closely related tasks and the evaluation of acquisition strategies, including a proposed probability-of-rare-class (PRC) approach. We perform these experiments for a specific rare class problem: collecting language samples of cognitive dissonance from social media. We find that PRC is a simple and effective strategy to guide annotations and ultimately improve model accuracy while transfer-learning in a specific order can improve the cold-start performance of the learner but does not benefit iterations of active learning.",
+}
+
+```
+
+