Create README.md

README.md

@@ -0,0 +1,159 @@
+annotations_creators:
+- machine-generated
+- crowdsourced
+- found
+language_creators:
+- machine-generated
+- crowdsourced
+languages: []
+licenses:
+- mit
+multilinguality:
+- monolingual
+pretty_name: QA2D
+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
+
+
+# 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:** [Needs More Information]
+- **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.
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+en
+
+## Dataset Structure
+
+### Data Instances
+
+[Needs More Information]
+
+### Data Fields
+
+[Needs More Information]
+
+### Data Splits
+
+[Needs More Information]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### 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}
+}