Datasets:
numer_sense

	---
	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
	---

	# Dataset Card for [Dataset Name]

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-instances)
	- [Data Splits](#data-instances)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)

	## 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](https://arxiv.org/abs/2005.00683)

	### 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](https://inklab.usc.edu/NumerSense/#exp) 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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/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](https://github.com/INK-USC/NumerSense/blob/main/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}
	}
	```