Add README

README.md

@@ -34,3 +34,44 @@ configs:
   - split: validation
     path: multieurlex-doc-en/validation-*
 ---
+
+## Additional Information
+
+### Licensing Information
+
+We provide MultiEURLEX with the same licensing as the original EU data (CC-BY-4.0):
+
+© European Union, 1998-2021
+
+The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.
+
+The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
+
+Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html \
+Read more:  https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html
+
+### Citation Information
+
+```
+@inproceedings{fujinuma-etal-2023-multi,
+    title = "A Multi-Modal Multilingual Benchmark for Document Image Classification",
+    author = "Fujinuma, Yoshinari  and
+      Varia, Siddharth  and
+      Sankaran, Nishant  and
+      Appalaraju, Srikar  and
+      Min, Bonan  and
+      Vyas, Yogarshi",
+    editor = "Bouamor, Houda  and
+      Pino, Juan  and
+      Bali, Kalika",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.findings-emnlp.958",
+    doi = "10.18653/v1/2023.findings-emnlp.958",
+    pages = "14361--14376",
+    abstract = "Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents such as forms, emails, and other such documents. We show that the only existing dataset for this task (Lewis et al., 2006) has several limitations and we introduce two newly curated multilingual datasets WIKI-DOC and MULTIEURLEX-DOC that overcome these limitations. We further undertake a comprehensive study of popular visually-rich document understanding or Document AI models in previously untested setting in document image classification such as 1) multi-label classification, and 2) zero-shot cross-lingual transfer setup. Experimental results show limitations of multilingual Document AI models on cross-lingual transfer across typologically distant languages. Our datasets and findings open the door for future research into improving Document AI models.",
+}
+```