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