Update README.md

README.md

@@ -39,6 +39,25 @@ Moreover, SpanEx is annotated by three annotators, which opens new avenues for s
 Our study reveals that while human annotators often agree on span interactions, they also offer complementary reasons for a prediction, collectively providing a comprehensive set of reasons for a prediction.
 We collect explanations of span interactions for NLI on the SNLI dataset and for FC on the FEVER dataset.
 
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+Please cite the following paper if you use this dataset:
 
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+```
+@inproceedings{choudhury-etal-2023-explaining,
+    title = "Explaining Interactions Between Text Spans",
+    author = "Choudhury, Sagnik  and
+      Atanasova, Pepa  and
+      Augenstein, Isabelle",
+    editor = "Bouamor, Houda  and
+      Pino, Juan  and
+      Bali, Kalika",
+    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.emnlp-main.783",
+    doi = "10.18653/v1/2023.emnlp-main.783",
+    pages = "12709--12730",
+    abstract = "Reasoning over spans of tokens from different parts of the input is essential for natural language understanding (NLU) tasks such as fact-checking (FC), machine reading comprehension (MRC) or natural language inference (NLI). However, existing highlight-based explanations primarily focus on identifying individual important features or interactions only between adjacent tokens or tuples of tokens. Most notably, there is a lack of annotations capturing the human decision-making process with respect to the necessary interactions for informed decision-making in such tasks. To bridge this gap, we introduce SpanEx, a multi-annotator dataset of human span interaction explanations for two NLU tasks: NLI and FC. We then investigate the decision-making processes of multiple fine-tuned large language models in terms of the employed connections between spans in separate parts of the input and compare them to the human reasoning processes. Finally, we present a novel community detection based unsupervised method to extract such interaction explanations. We make the code and the dataset available on [Github](https://github.com/copenlu/spanex). The dataset is also available on [Huggingface datasets](https://huggingface.co/datasets/copenlu/spanex).",
+}
+```