Dataset Card for "asnq"

Dataset Summary

ASNQ is a dataset for answer sentence selection derived from Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).

Each example contains a question, candidate sentence, label indicating whether or not the sentence answers the question, and two additional features -- sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the candidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.

For more details please see https://arxiv.org/abs/1911.04118

and

https://research.google/pubs/pub47761/

Supported Tasks and Leaderboards

More Information Needed

Languages

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Dataset Structure

Data Instances

default

• Size of downloaded dataset files: 3.56 GB
• Size of the generated dataset: 3.82 GB
• Total amount of disk used: 7.39 GB

An example of 'validation' looks as follows.

{
    "label": 0,
    "question": "when did somewhere over the rainbow come out",
    "sentence": "In films and TV shows ( edit ) In the film Third Finger , Left Hand ( 1940 ) with Myrna Loy , Melvyn Douglas , and Raymond Walburn , the tune played throughout the film in short sequences .",
    "sentence_in_long_answer": false,
    "short_answer_in_sentence": false
}

Data Fields

The data fields are the same among all splits.

default

• question : a string feature.
• sentence : a string feature.
• label : a classification label, with possible values including neg (0), pos (1).
• sentence_in_long_answer : a bool feature.
• short_answer_in_sentence : a bool feature.

Data Splits

name	train	validation
default	20377568	930062

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

The data is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License: https://github.com/alexa/wqa_tanda/blob/master/LICENSE

Citation Information

@article{Garg_2020,
   title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},
   volume={34},
   ISSN={2159-5399},
   url={http://dx.doi.org/10.1609/AAAI.V34I05.6282},
   DOI={10.1609/aaai.v34i05.6282},
   number={05},
   journal={Proceedings of the AAAI Conference on Artificial Intelligence},
   publisher={Association for the Advancement of Artificial Intelligence (AAAI)},
   author={Garg, Siddhant and Vu, Thuy and Moschitti, Alessandro},
   year={2020},
   month={Apr},
   pages={7780–7788}
}

Contributions

Thanks to @mkserge for adding this dataset.