
# 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.

# 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",
}
```
task_categories:question-answering task_categories:multiple-choice task_ids:open-domain-qa task_ids:multiple-choice-qa annotations_creators:expert-generated language_creators:crowdsourced multilinguality:monolingual size_categories:n<1K source_datasets:original language:en license:apache-2.0 region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["question-answering", "multiple-choice"], "task_ids": ["open-domain-qa", "multiple-choice-qa"], "pretty_name": "discourse_marker_qa"}
TAGS
#task_categories-question-answering #task_categories-multiple-choice #task_ids-open-domain-qa #task_ids-multiple-choice-qa #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-apache-2.0 #region-us 


# 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

Design considerations:
<URL

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

Design considerations:
<URL

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.
TAGS
#task_categories-question-answering #task_categories-multiple-choice #task_ids-open-domain-qa #task_ids-multiple-choice-qa #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-apache-2.0 #region-us 
 # 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

Design considerations:
<URL

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.
passage: TAGS
#task_categories-question-answering #task_categories-multiple-choice #task_ids-open-domain-qa #task_ids-multiple-choice-qa #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-apache-2.0 #region-us 
# 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

Design considerations:
<URL

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.