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license: apache-2.0
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---
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license: apache-2.0
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# Dataset for evaluation of (zero-shot) discourse marker prediction with language models
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This is the Big-Bench version of our language-based movie recommendation dataset
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<https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/discourse_marker_prediction>
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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.
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# Citation
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```
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@inproceedings{sileo-etal-2019-mining,
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title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning",
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author = "Sileo, Damien and
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Van De Cruys, Tim and
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Pradel, Camille and
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Muller, Philippe",
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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)",
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month = jun,
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year = "2019",
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address = "Minneapolis, Minnesota",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/N19-1351",
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doi = "10.18653/v1/N19-1351",
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pages = "3477--3486",
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}
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```
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