|
--- |
|
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", |
|
} |
|
``` |
