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README.md

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----
-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}
-}

dataset_infos.json

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