Update info

README.md

@@ -4,38 +4,36 @@ task_categories:
 language:
 - en
 tags:
-- reddit
+- stackexchange
 - law
-pretty_name: Legal Advice Reddit
+pretty_name: Law Stack Exchange
 ---
 
-# Dataset Card for Legal Advice Reddit Dataset
+# Dataset Card for Law Stack Exchange Dataset
 
 ## Dataset Description
 
-- **Paper: [Parameter-Efficient Legal Domain Adaptation](https://arxiv.org/abs/2210.13712)** 
+- **Paper: [Parameter-Efficient Legal Domain Adaptation](https://aclanthology.org/2022.nllp-1.10/)** 
 - **Point of Contact: [email protected]** 
 
 ### Dataset Summary
 
-We introduce a new dataset from the Legal Advice Reddit community (known as "/r/legaldvice"), sourcing the Reddit posts from the Pushshift
-Reddit dataset. The dataset maps the text and title of each legal question posted into one of eleven classes, based on the original Reddit
-post's "flair" (i.e., tag). Questions are typically informal and use non-legal-specific language. Per the Legal Advice Reddit rules, posts 
-must be about actual personal circumstances or situations. We limit the number of labels to the top eleven classes and remove the other 
-samples from the dataset.
-
+Dataset from the Law Stack Exchange, as used in "Parameter-Efficient Legal Domain Adaptation".
 
 ### Citation Information
 
 ```
-@misc{https://doi.org/10.48550/arxiv.2210.13712,
-  doi = {10.48550/ARXIV.2210.13712},
-  url = {https://arxiv.org/abs/2210.13712},
-  author = {Li, Jonathan and Bhambhoria, Rohan and Zhu, Xiaodan},
-  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
-  title = {Parameter-Efficient Legal Domain Adaptation},
-  publisher = {arXiv},
-  year = {2022},
-  copyright = {arXiv.org perpetual, non-exclusive license}
+@inproceedings{li-etal-2022-parameter,
+    title = "Parameter-Efficient Legal Domain Adaptation",
+    author = "Li, Jonathan  and
+      Bhambhoria, Rohan  and
+      Zhu, Xiaodan",
+    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.nllp-1.10",
+    pages = "119--129",
 }
 ```