Datasets:
Tasks:
Text2Text Generation
Modalities:
Text
Formats:
parquet
Sub-tasks:
text-simplification
Size:
10K - 100K
ArXiv:
License:
| annotations_creators: | |
| - machine-generated | |
| - crowdsourced | |
| - found | |
| language_creators: | |
| - machine-generated | |
| - crowdsourced | |
| language: [] | |
| license: | |
| - mit | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - original | |
| - extended|squad | |
| - extended|race | |
| - extended|newsqa | |
| - extended|qamr | |
| - extended|movieQA | |
| task_categories: | |
| - text2text-generation | |
| task_ids: | |
| - text-simplification | |
| pretty_name: QA2D | |
| # Dataset Card for QA2D | |
| ## Table of Contents | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-instances) | |
| - [Data Splits](#data-instances) | |
| - [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) | |
| ## Dataset Description | |
| - **Homepage:** https://worksheets.codalab.org/worksheets/0xd4ebc52cebb84130a07cbfe81597aaf0/ | |
| - **Repository:** https://github.com/kelvinguu/qanli | |
| - **Paper:** https://arxiv.org/abs/1809.02922 | |
| - **Leaderboard:** [Needs More Information] | |
| - **Point of Contact:** [Needs More Information] | |
| ### Dataset Summary | |
| Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets. | |
| This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets. | |
| ### Supported Tasks and Leaderboards | |
| [Needs More Information] | |
| ### Languages | |
| en | |
| ## Dataset Structure | |
| ### Data Instances | |
| See below. | |
| ### Data Fields | |
| - `dataset`: lowercased name of dataset (movieqa, newsqa, qamr, race, squad) | |
| - `example_uid`: unique id of example within dataset (there are examples with the same uids from different datasets, so the combination of dataset + example_uid should be used for unique indexing) | |
| - `question`: tokenized (space-separated) question from the source QA dataset | |
| - `answer`: tokenized (space-separated) answer span from the source QA dataset | |
| - `turker_answer`: tokenized (space-separated) answer sentence collected from MTurk | |
| - `rule-based`: tokenized (space-separated) answer sentence, generated by the rule-based model | |
| ### Data Splits | |
| | Dataset Split | Number of Instances in Split | | |
| | ------------- |----------------------------- | | |
| | Train | 60,710 | | |
| | Dev | 10,344 | | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets. | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| [Needs More Information] | |
| #### Who are the source language producers? | |
| [Needs More Information] | |
| ### Annotations | |
| #### Annotation process | |
| [Needs More Information] | |
| #### Who are the annotators? | |
| [Needs More Information] | |
| ### Personal and Sensitive Information | |
| [Needs More Information] | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [Needs More Information] | |
| ### Discussion of Biases | |
| [Needs More Information] | |
| ### Other Known Limitations | |
| [Needs More Information] | |
| ## Additional Information | |
| ### Dataset Curators | |
| [Needs More Information] | |
| ### Licensing Information | |
| [Needs More Information] | |
| ### Citation Information | |
| @article{DBLP:journals/corr/abs-1809-02922, | |
| author = {Dorottya Demszky and | |
| Kelvin Guu and | |
| Percy Liang}, | |
| title = {Transforming Question Answering Datasets Into Natural Language Inference | |
| Datasets}, | |
| journal = {CoRR}, | |
| volume = {abs/1809.02922}, | |
| year = {2018}, | |
| url = {http://arxiv.org/abs/1809.02922}, | |
| eprinttype = {arXiv}, | |
| eprint = {1809.02922}, | |
| timestamp = {Fri, 05 Oct 2018 11:34:52 +0200}, | |
| biburl = {https://dblp.org/rec/journals/corr/abs-1809-02922.bib}, | |
| bibsource = {dblp computer science bibliography, https://dblp.org} | |
| } |