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sileod
/

discourse_marker_qa

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Question Answering

Multiple Choice

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Sub-tasks:

multiple-choice-qa

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sileod commited on May 27, 2022

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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.

 # Dataset for evaluation of (zero-shot) discourse marker prediction with language models
+This is the Big-Bench version of our discourse marker prediction dataset, [Discovery](https://huggingface.co/datasets/discovery)
+Design considerations:
 <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.