Datasets:
Tasks:
Question Answering
Sub-tasks:
multiple-choice-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
Tags:
License:
| annotations_creators: | |
| - crowdsourced | |
| language_creators: | |
| - crowdsourced | |
| language: | |
| - en | |
| license: odc-by | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 1K<n<10K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - question-answering | |
| task_ids: | |
| - multiple-choice-qa | |
| paperswithcode_id: codah | |
| pretty_name: COmmonsense Dataset Adversarially-authored by Humans | |
| dataset_info: | |
| - config_name: codah | |
| features: | |
| - name: id | |
| dtype: int32 | |
| - name: question_category | |
| dtype: | |
| class_label: | |
| names: | |
| '0': Idioms | |
| '1': Reference | |
| '2': Polysemy | |
| '3': Negation | |
| '4': Quantitative | |
| '5': Others | |
| - name: question_propmt | |
| dtype: string | |
| - name: candidate_answers | |
| sequence: string | |
| - name: correct_answer_idx | |
| dtype: int32 | |
| splits: | |
| - name: train | |
| num_bytes: 571196 | |
| num_examples: 2776 | |
| download_size: 352902 | |
| dataset_size: 571196 | |
| - config_name: fold_0 | |
| features: | |
| - name: id | |
| dtype: int32 | |
| - name: question_category | |
| dtype: | |
| class_label: | |
| names: | |
| '0': Idioms | |
| '1': Reference | |
| '2': Polysemy | |
| '3': Negation | |
| '4': Quantitative | |
| '5': Others | |
| - name: question_propmt | |
| dtype: string | |
| - name: candidate_answers | |
| sequence: string | |
| - name: correct_answer_idx | |
| dtype: int32 | |
| splits: | |
| - name: train | |
| num_bytes: 344900 | |
| num_examples: 1665 | |
| - name: validation | |
| num_bytes: 114199 | |
| num_examples: 556 | |
| - name: test | |
| num_bytes: 112097 | |
| num_examples: 555 | |
| download_size: 379179 | |
| dataset_size: 571196 | |
| - config_name: fold_1 | |
| features: | |
| - name: id | |
| dtype: int32 | |
| - name: question_category | |
| dtype: | |
| class_label: | |
| names: | |
| '0': Idioms | |
| '1': Reference | |
| '2': Polysemy | |
| '3': Negation | |
| '4': Quantitative | |
| '5': Others | |
| - name: question_propmt | |
| dtype: string | |
| - name: candidate_answers | |
| sequence: string | |
| - name: correct_answer_idx | |
| dtype: int32 | |
| splits: | |
| - name: train | |
| num_bytes: 340978 | |
| num_examples: 1665 | |
| - name: validation | |
| num_bytes: 114199 | |
| num_examples: 556 | |
| - name: test | |
| num_bytes: 116019 | |
| num_examples: 555 | |
| download_size: 379728 | |
| dataset_size: 571196 | |
| - config_name: fold_2 | |
| features: | |
| - name: id | |
| dtype: int32 | |
| - name: question_category | |
| dtype: | |
| class_label: | |
| names: | |
| '0': Idioms | |
| '1': Reference | |
| '2': Polysemy | |
| '3': Negation | |
| '4': Quantitative | |
| '5': Others | |
| - name: question_propmt | |
| dtype: string | |
| - name: candidate_answers | |
| sequence: string | |
| - name: correct_answer_idx | |
| dtype: int32 | |
| splits: | |
| - name: train | |
| num_bytes: 342281 | |
| num_examples: 1665 | |
| - name: validation | |
| num_bytes: 114199 | |
| num_examples: 556 | |
| - name: test | |
| num_bytes: 114716 | |
| num_examples: 555 | |
| download_size: 379126 | |
| dataset_size: 571196 | |
| - config_name: fold_3 | |
| features: | |
| - name: id | |
| dtype: int32 | |
| - name: question_category | |
| dtype: | |
| class_label: | |
| names: | |
| '0': Idioms | |
| '1': Reference | |
| '2': Polysemy | |
| '3': Negation | |
| '4': Quantitative | |
| '5': Others | |
| - name: question_propmt | |
| dtype: string | |
| - name: candidate_answers | |
| sequence: string | |
| - name: correct_answer_idx | |
| dtype: int32 | |
| splits: | |
| - name: train | |
| num_bytes: 342832 | |
| num_examples: 1665 | |
| - name: validation | |
| num_bytes: 114199 | |
| num_examples: 556 | |
| - name: test | |
| num_bytes: 114165 | |
| num_examples: 555 | |
| download_size: 379178 | |
| dataset_size: 571196 | |
| - config_name: fold_4 | |
| features: | |
| - name: id | |
| dtype: int32 | |
| - name: question_category | |
| dtype: | |
| class_label: | |
| names: | |
| '0': Idioms | |
| '1': Reference | |
| '2': Polysemy | |
| '3': Negation | |
| '4': Quantitative | |
| '5': Others | |
| - name: question_propmt | |
| dtype: string | |
| - name: candidate_answers | |
| sequence: string | |
| - name: correct_answer_idx | |
| dtype: int32 | |
| splits: | |
| - name: train | |
| num_bytes: 342832 | |
| num_examples: 1665 | |
| - name: validation | |
| num_bytes: 114165 | |
| num_examples: 555 | |
| - name: test | |
| num_bytes: 114199 | |
| num_examples: 556 | |
| download_size: 379178 | |
| dataset_size: 571196 | |
| configs: | |
| - config_name: codah | |
| data_files: | |
| - split: train | |
| path: codah/train-* | |
| - config_name: fold_0 | |
| data_files: | |
| - split: train | |
| path: fold_0/train-* | |
| - split: validation | |
| path: fold_0/validation-* | |
| - split: test | |
| path: fold_0/test-* | |
| - config_name: fold_1 | |
| data_files: | |
| - split: train | |
| path: fold_1/train-* | |
| - split: validation | |
| path: fold_1/validation-* | |
| - split: test | |
| path: fold_1/test-* | |
| - config_name: fold_2 | |
| data_files: | |
| - split: train | |
| path: fold_2/train-* | |
| - split: validation | |
| path: fold_2/validation-* | |
| - split: test | |
| path: fold_2/test-* | |
| - config_name: fold_3 | |
| data_files: | |
| - split: train | |
| path: fold_3/train-* | |
| - split: validation | |
| path: fold_3/validation-* | |
| - split: test | |
| path: fold_3/test-* | |
| - config_name: fold_4 | |
| data_files: | |
| - split: train | |
| path: fold_4/train-* | |
| - split: validation | |
| path: fold_4/validation-* | |
| - split: test | |
| path: fold_4/test-* | |
| # Dataset Card for COmmonsense Dataset Adversarially-authored by Humans | |
| ## Table of Contents | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** [Add homepage URL here if available (unless it's a GitHub repository)]() | |
| - **Repository:** https://github.com/Websail-NU/CODAH | |
| - **Paper:** https://aclanthology.org/W19-2008/ | |
| - **Paper:** https://arxiv.org/abs/1904.04365 | |
| ### Dataset Summary | |
| The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense | |
| question-answering in the sentence completion style of SWAG. As opposed to other automatically generated | |
| NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model | |
| and use this information to design challenging commonsense questions. | |
| ### Supported Tasks and Leaderboards | |
| [More Information Needed] | |
| ### Languages | |
| [More Information Needed] | |
| ## Dataset Structure | |
| ### Data Instances | |
| [More Information Needed] | |
| ### Data Fields | |
| [More Information Needed] | |
| ### Data Splits | |
| [More Information Needed] | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| [More Information Needed] | |
| ### Source Data | |
| [More Information Needed] | |
| #### Initial Data Collection and Normalization | |
| [More Information Needed] | |
| #### Who are the source language producers? | |
| [More Information Needed] | |
| ### Annotations | |
| [More Information Needed] | |
| #### Annotation process | |
| [More Information Needed] | |
| #### Who are the annotators? | |
| [More Information Needed] | |
| ### Personal and Sensitive Information | |
| [More Information Needed] | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed] | |
| ### Discussion of Biases | |
| [More Information Needed] | |
| ### Other Known Limitations | |
| [More Information Needed] | |
| ## Additional Information | |
| ### Dataset Curators | |
| [More Information Needed] | |
| ### Licensing Information | |
| The CODAH dataset is made available under the Open Data Commons Attribution License: http://opendatacommons.org/licenses/by/1.0/ | |
| ### Citation Information | |
| ``` | |
| @inproceedings{chen-etal-2019-codah, | |
| title = "{CODAH}: An Adversarially-Authored Question Answering Dataset for Common Sense", | |
| author = "Chen, Michael and | |
| D{'}Arcy, Mike and | |
| Liu, Alisa and | |
| Fernandez, Jared and | |
| Downey, Doug", | |
| editor = "Rogers, Anna and | |
| Drozd, Aleksandr and | |
| Rumshisky, Anna and | |
| Goldberg, Yoav", | |
| booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}", | |
| month = jun, | |
| year = "2019", | |
| address = "Minneapolis, USA", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/W19-2008", | |
| doi = "10.18653/v1/W19-2008", | |
| pages = "63--69", | |
| abstract = "Commonsense reasoning is a critical AI capability, but it is difficult to construct challenging datasets that test common sense. Recent neural question answering systems, based on large pre-trained models of language, have already achieved near-human-level performance on commonsense knowledge benchmarks. These systems do not possess human-level common sense, but are able to exploit limitations of the datasets to achieve human-level scores. We introduce the CODAH dataset, an adversarially-constructed evaluation dataset for testing common sense. CODAH forms a challenging extension to the recently-proposed SWAG dataset, which tests commonsense knowledge using sentence-completion questions that describe situations observed in video. To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems. Workers are rewarded for submissions that models fail to answer correctly both before and after fine-tuning (in cross-validation). We create 2.8k questions via this procedure and evaluate the performance of multiple state-of-the-art question answering systems on our dataset. We observe a significant gap between human performance, which is 95.3{\%}, and the performance of the best baseline accuracy of 65.3{\%} by the OpenAI GPT model.", | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. |