Hugging Face's logo

Datasets:
swag

Tasks:
Text Classification
Sub-tasks:
natural-language-inference
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
crowdsourced
machine-generated
Source Datasets:
original
ArXiv:
Tags:
License:
Dataset card Viewer Files Community
3
swag / README.md
albertvillanova's picture
albertvillanova HF staff
Replace YAML keys from int to str (#2)
bd65528
preview code
|
raw history blame contribute delete
No virus
8.88 kB
metadata
annotations_creators:
  - crowdsourced
  - machine-generated
language_creators:
  - found
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - natural-language-inference
paperswithcode_id: swag
pretty_name: Situations With Adversarial Generations
dataset_info:
  - config_name: regular
    features:
      - name: video-id
        dtype: string
      - name: fold-ind
        dtype: string
      - name: startphrase
        dtype: string
      - name: sent1
        dtype: string
      - name: sent2
        dtype: string
      - name: gold-source
        dtype: string
      - name: ending0
        dtype: string
      - name: ending1
        dtype: string
      - name: ending2
        dtype: string
      - name: ending3
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': '0'
              '1': '1'
              '2': '2'
              '3': '3'
    splits:
      - name: train
        num_bytes: 30274672
        num_examples: 73546
      - name: validation
        num_bytes: 8451771
        num_examples: 20006
      - name: test
        num_bytes: 8417644
        num_examples: 20005
    download_size: 43954806
    dataset_size: 47144087
  - config_name: full
    features:
      - name: video-id
        dtype: string
      - name: fold-ind
        dtype: string
      - name: startphrase
        dtype: string
      - name: gold-ending
        dtype: string
      - name: distractor-0
        dtype: string
      - name: distractor-1
        dtype: string
      - name: distractor-2
        dtype: string
      - name: distractor-3
        dtype: string
      - name: gold-source
        dtype: string
      - name: gold-type
        dtype: string
      - name: distractor-0-type
        dtype: string
      - name: distractor-1-type
        dtype: string
      - name: distractor-2-type
        dtype: string
      - name: distractor-3-type
        dtype: string
      - name: sent1
        dtype: string
      - name: sent2
        dtype: string
    splits:
      - name: train
        num_bytes: 34941649
        num_examples: 73546
      - name: validation
        num_bytes: 9832603
        num_examples: 20006
    download_size: 40537624
    dataset_size: 44774252

Dataset Card for Situations With Adversarial Generations

Table of Contents

Dataset Description

Dataset Summary

Given a partial description like "she opened the hood of the car," humans can reason about the situation and anticipate what might come next ("then, she examined the engine"). SWAG (Situations With Adversarial Generations) is a large-scale dataset for this task of grounded commonsense inference, unifying natural language inference and physically grounded reasoning.

The dataset consists of 113k multiple choice questions about grounded situations (73k training, 20k validation, 20k test). Each question is a video caption from LSMDC or ActivityNet Captions, with four answer choices about what might happen next in the scene. The correct answer is the (real) video caption for the next event in the video; the three incorrect answers are adversarially generated and human verified, so as to fool machines but not humans. SWAG aims to be a benchmark for evaluating grounded commonsense NLI and for learning representations.

Supported Tasks and Leaderboards

The dataset introduces the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning.

Languages

The text in the dataset is in English. The associated BCP-47 code is en.

Dataset Structure

Data Instances

The regular configuration should be used for modeling. An example looks like this:

{
  "video-id": "anetv_dm5WXFiQZUQ",
  "fold-ind": "18419",
  "startphrase", "He rides the motorcycle down the hall and into the elevator. He",
  "sent1": "He rides the motorcycle down the hall and into the elevator."
  "sent2": "He",
  "gold-source": "gold",
  "ending0": "looks at a mirror in the mirror as he watches someone walk through a door.",
  "ending1": "stops, listening to a cup of coffee with the seated woman, who's standing.",
  "ending2": "exits the building and rides the motorcycle into a casino where he performs several tricks as people watch.",
  "ending3": "pulls the bag out of his pocket and hands it to someone's grandma.",
  "label": 2,
}

Note that the test are reseved for blind submission on the leaderboard.

The full train and validation sets provide more information regarding the collection process.

Data Fields

  • video-id: identification
  • fold-ind: identification
  • startphrase: the context to be filled
  • sent1: the first sentence
  • sent2: the start of the second sentence (to be filled)
  • gold-source: generated or comes from the found completion
  • ending0: first proposition
  • ending1: second proposition
  • ending2: third proposition
  • ending3: fourth proposition
  • label: the correct proposition

More info concerning the fields can be found on the original repo.

Data Splits

The dataset consists of 113k multiple choice questions about grounded situations: 73k for training, 20k for validation, and 20k for (blind) test.

Dataset Creation

Curation Rationale

The authors seek dataset diversity while minimizing annotation artifacts, conditional stylistic patterns such as length and word-preference biases. To avoid introducing easily “gamed” patterns, they introduce Adversarial Filtering (AF), a generally- applicable treatment involving the iterative refinement of a set of assignments to increase the entropy under a chosen model family. The dataset is then human verified by paid crowdsourcers.

Source Data

This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)

Initial Data Collection and Normalization

The dataset is derived from pairs of consecutive video captions from ActivityNet Captions and the Large Scale Movie Description Challenge. The two datasets are slightly different in nature and allow us to achieve broader coverage: ActivityNet contains 20k YouTube clips containing one of 203 activity types (such as doing gymnastics or playing guitar); LSMDC consists of 128k movie captions (audio descriptions and scripts).

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

Annotations are first machine generated and then adversarially filtered. Finally, the remaining examples are human-verified by paid crowdsourcers.

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

Unknown

Citation Information 

@inproceedings{zellers2018swagaf,
    title={SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference},
    author={Zellers, Rowan and Bisk, Yonatan and Schwartz, Roy and Choi, Yejin},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    year={2018}
}

Contributions

Thanks to @VictorSanh for adding this dataset.