Update README.md

README.md

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 ---
 license: apache-2.0
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+
+# Dataset for evaluation of (zero-shot) discourse marker prediction with language models
+
+This is the Big-Bench version of our language-based movie recommendation dataset
+
+<https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/discourse_marker_prediction>
+
+GPT2 has to zero-shot 15% accuracy with on this multiple-choice task based on language modeling perplexity. As a comparison, a fully supervised model, trained with 10k examples per marker with ROBERTA and default hyperparameters with one epoch, leads to an accuracy of 30% with 174 possible markers. This shows that this task is hard for GPT2 and that the model didn't memorize the discourse markers, but that high accuracies are still possible.
+
+# Citation
+```
+@inproceedings{sileo-etal-2019-mining,
+    title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning",
+    author = "Sileo, Damien  and
+      Van De Cruys, Tim  and
+      Pradel, Camille  and
+      Muller, Philippe",
+    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
+    month = jun,
+    year = "2019",
+    address = "Minneapolis, Minnesota",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/N19-1351",
+    doi = "10.18653/v1/N19-1351",
+    pages = "3477--3486",
+}
+```