Datasets:
asnq

Languages: en

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/pdf/1911.04118.pdf

and

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

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

default

  • Size of downloaded dataset files: 3398.76 MB
  • Size of the generated dataset: 3647.70 MB
  • Total amount of disk used: 7046.46 MB

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

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Citation Information

@article{garg2019tanda,
    title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},
    author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},
    year={2019},
    eprint={1911.04118},
}

Contributions

Thanks to @mkserge for adding this dataset.

Models trained or fine-tuned on asnq

None yet