Create README.md

README.md

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+---
+language:
+- fr
+- en
+- es
+categories:
+- translation
+multilinguality:
+- multilingual
+---
+
+> [!NOTE]
+> Dataset origin: http://lrec2016.lrec-conf.org/en/shared-lrs/
+
+## Description
+
+ 	
+This dataset is made available for evaluation of cross-lingual similarity detection algorithms.  
+The characteristics of the dataset are the following:
+- it is multilingual: French, English and Spanish;
+- it proposes cross-language alignment information at different granularities: document-level, sentence-level and chunk-level;
+- it is based on both parallel and comparable corpora;
+- it contains both human and machine translated text;
+- part of it has been altered (to make the cross-language similarity detection more complicated) while the rest remains without noise;
+- documents were written by multiple types of authors: from average to professionals.
+
+## Citation
+```
+@InProceedings{FERRERO16.304,
+  author = {Jérémy Ferrero and Frédéric Agnès and Laurent Besacier and Didier Schwab},
+  title = {A Multilingual, Multi-style and Multi-granularity Dataset for Cross-language Textual Similarity Detection},
+  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
+  year = {2016},
+  month = {may},
+  date = {23-28},
+  location = {Portorož, Slovenia},
+  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
+  publisher = {European Language Resources Association (ELRA)},
+  address = {Paris, France},
+  isbn = {978-2-9517408-9-1},
+  language = {english}
+ }  
+```