Datasets:

jhu-cogsci
/

hans

Tasks:

Text Classification

Sub-tasks:

natural-language-inference

Languages:

English

Size:

10K<n<100K

ArXiv:

License:

Dataset card Files Files and versions

xet

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Dataset Card for "hans"

Dataset Summary

The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

plain_text

Size of downloaded dataset files: 30.94 MB
Size of the generated dataset: 31.81 MB
Total amount of disk used: 62.76 MB

An example of 'train' looks as follows.

Data Fields

The data fields are the same among all splits.

plain_text

premise: a string feature.
hypothesis: a string feature.
label: a classification label, with possible values including entailment (0), non-entailment (1).
parse_premise: a string feature.
parse_hypothesis: a string feature.
binary_parse_premise: a string feature.
binary_parse_hypothesis: a string feature.
heuristic: a string feature.
subcase: a string feature.
template: a string feature.

Data Splits

name	train	validation
plain_text	30000	30000

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

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

More Information Needed

Citation Information

@article{DBLP:journals/corr/abs-1902-01007,
  author    = {R. Thomas McCoy and
               Ellie Pavlick and
               Tal Linzen},
  title     = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural
               Language Inference},
  journal   = {CoRR},
  volume    = {abs/1902.01007},
  year      = {2019},
  url       = {http://arxiv.org/abs/1902.01007},
  archivePrefix = {arXiv},
  eprint    = {1902.01007},
  timestamp = {Tue, 21 May 2019 18:03:36 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1902-01007.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}