README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - crowdsourced
languages:
  - en
licenses:
  - mit
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - extended|other
task_categories:
  - sequence-modeling
task_ids:
  - slot-filling
paperswithcode_id: numersense
pretty_name: NumerSense

Dataset Card for [Dataset Name]

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://inklab.usc.edu/NumerSense/
• Repository: https://github.com/INK-USC/NumerSense
• Paper: https://arxiv.org/abs/2005.00683
• Leaderboard: https://inklab.usc.edu/NumerSense/#exp
• Point of Contact: Author emails listed in paper

Dataset Summary

NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145 masked-word-prediction probes. The general idea is to mask numbers between 0-10 in sentences mined from a commonsense corpus and evaluate whether a language model can correctly predict the masked value.

Supported Tasks and Leaderboards

The dataset supports the task of slot-filling, specifically as an evaluation of numerical common sense. A leaderboard is included on the dataset webpage with included benchmarks for GPT-2, RoBERTa, BERT, and human performance. Leaderboards are included for both the core set and the adversarial set discussed below.

Languages

This dataset is in English.

Dataset Structure

Data Instances

Each instance consists of a sentence with a masked numerical value between 0-10 and (in the train set) a target. Example from the training set:

sentence: Black bears are about <mask> metres tall.
target: two

Data Fields

Each value of the training set consists of:

• sentence: The sentence with a number masked out with the <mask> token.
• target: The ground truth target value. Since the test sets do not include the ground truth, the target field values are empty strings in the test_core and test_all splits.

Data Splits

The dataset includes the following pre-defined data splits:

• A train set with >10K labeled examples (i.e. containing a ground truth value)
• A core test set (test_core) with 1,132 examples (no ground truth provided)
• An expanded test set (test_all) encompassing test_core with the addition of adversarial examples for a total of 3,146 examples. See section 2.2 of [the paper] for a discussion of how these examples are constructed.

Dataset Creation

Curation Rationale

The purpose of this dataset is "to study whether PTLMs capture numerical commonsense knowledge, i.e., commonsense knowledge that provides an understanding of the numeric relation between entities." This work is motivated by the prior research exploring whether language models possess commonsense knowledge.

Source Data

Initial Data Collection and Normalization

The dataset is an extension of the Open Mind Common Sense corpus. A query was performed to discover sentences containing numbers between 0-12, after which the resulting sentences were manually evaluated for inaccuracies, typos, and the expression of commonsense knowledge. The numerical values were then masked.

Who are the source language producers?

The Open Mind Common Sense corpus, from which this dataset is sourced, is a crowdsourced dataset maintained by the MIT Media Lab.

Annotations

Annotation process

No annotations are present in this dataset beyond the target values automatically sourced from the masked sentences, as discussed above.

Who are the annotators?

The curation and inspection was done in two rounds by graduate students.

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

The motivation of measuring a model's ability to associate numerical values with real-world concepts appears relatively innocuous. However, as discussed in the following section, the source dataset may well have biases encoded from crowdworkers, particularly in terms of factoid coverage. A model's ability to perform well on this benchmark should therefore not be considered evidence that it is more unbiased or objective than a human performing similar tasks.

[More Information Needed]

Discussion of Biases

This dataset is sourced from a crowdsourced commonsense knowledge base. While the information contained in the graph is generally considered to be of high quality, the coverage is considered to very low as a representation of all possible commonsense knowledge. The representation of certain factoids may also be skewed by the demographics of the crowdworkers. As one possible example, the term "homophobia" is connected with "Islam" in the ConceptNet knowledge base, but not with any other religion or group, possibly due to the biases of crowdworkers contributing to the project.

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

This dataset was collected by Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren, Computer Science researchers at the at the University of Southern California.

Licensing Information

The data is hosted in a GitHub repositor with the MIT License.

Citation Information

@inproceedings{lin2020numersense,
  title={Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models},
  author={Bill Yuchen Lin and Seyeon Lee and Rahul Khanna and Xiang Ren}, 
  booktitle={Proceedings of EMNLP},
  year={2020},
  note={to appear}
}

Contributions

Thanks to @joeddav for adding this dataset.