File size: 7,007 Bytes
27ea105
18c0514
27ea105
 
 
 
3efd2bb
27ea105
3efd2bb
9dc3cee
27ea105
 
 
 
 
 
 
17ad675
05c461f
bf0e508
05c461f
 
c3bf57b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c1d0d9c
 
 
c3bf57b
 
 
 
 
27ea105
 
 
 
 
 
 
bf0e508
27ea105
 
 
bf0e508
 
27ea105
 
 
 
 
 
 
 
 
 
 
 
 
77de19d
27ea105
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
24288cf
 
 
 
27ea105
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
77de19d
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
---
pretty_name: ATOMIC
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: atomic
tags:
- common-sense-if-then-reasoning
dataset_info:
  features:
  - name: event
    dtype: string
  - name: oEffect
    sequence: string
  - name: oReact
    sequence: string
  - name: oWant
    sequence: string
  - name: xAttr
    sequence: string
  - name: xEffect
    sequence: string
  - name: xIntent
    sequence: string
  - name: xNeed
    sequence: string
  - name: xReact
    sequence: string
  - name: xWant
    sequence: string
  - name: prefix
    sequence: string
  - name: split
    dtype: string
  config_name: atomic
  splits:
  - name: train
    num_bytes: 32441878
    num_examples: 202271
  - name: test
    num_bytes: 3995624
    num_examples: 24856
  - name: validation
    num_bytes: 3629768
    num_examples: 22620
  download_size: 19083782
  dataset_size: 40067270
---

# Dataset Card for An Atlas of Machine Commonsense for If-Then Reasoning - Atomic Common Sense Dataset

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [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)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:**
https://homes.cs.washington.edu/~msap/atomic/
- **Repository:**
https://homes.cs.washington.edu/~msap/atomic/
- **Paper:**
Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith & Yejin Choi (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI

### Dataset Summary

This dataset provides the template sentences and
relationships defined in the ATOMIC common sense dataset. There are
three splits - train, test, and dev.

From the authors.

Disclaimer/Content warning: the events in atomic have been
automatically extracted from blogs, stories and books written at
various times. The events might depict violent or problematic actions,
which we left in the corpus for the sake of learning the (probably
negative but still important) commonsense implications associated with
the events. We removed a small set of truly out-dated events, but
might have missed some so please email us (msap@cs.washington.edu) if
you have any concerns.


For more information, see: https://homes.cs.washington.edu/~msap/atomic/

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages
en

## Dataset Structure

### Data Instances

Here is one example from the atomic dataset:


`` 
{'event': "PersonX uses PersonX's ___ to obtain", 'oEffect': [], 'oReact': ['annoyed', 'angry', 'worried'], 'oWant': [], 'prefix': ['uses', 'obtain'], 'split': 'trn', 'xAttr': [], 'xEffect': [], 'xIntent': ['to have an advantage', 'to fulfill a desire', 'to get out of trouble'], 'xNeed': [], 'xReact': ['pleased', 'smug', 'excited'], 'xWant': []}
``


### Data Fields

Notes from the authors:

* event: just a string representation of the event.
* oEffect,oReact,oWant,xAttr,xEffect,xIntent,xNeed,xReact,xWant: annotations for each of the dimensions, stored in a json-dumped string.
  Note: "none" means the worker explicitly responded with the empty response, whereas [] means the worker did not annotate this dimension.
* prefix: json-dumped string that represents the prefix of content words (used to make a better trn/dev/tst split).
* split: string rep of which split the event belongs to.

### Data Splits

The atomic dataset has three splits: test, train and dev of the form:

## Dataset Creation

### Curation Rationale

This dataset was gathered and created over to assist in common sense reasoning.

### Source Data

#### Initial Data Collection and Normalization

See the reaserch paper and website for more detail. The dataset was
created by the University of Washington using crowd sourced data


#### Who are the source language producers?

The Atomic authors and crowd source.

### Annotations

#### Annotation process

Human annotations directed by forms.

#### Who are the annotators?

Human annotations.

### Personal and Sensitive Information

Unkown, but likely none.

## Considerations for Using the Data

### Social Impact of Dataset

The goal for the work is to help machines understand common sense.

### Discussion of Biases

Since the data is human annotators, there is likely to be baised. From the authors:


Disclaimer/Content warning: the events in atomic have been automatically extracted from blogs, stories and books written at various times. The events might depict violent or problematic actions, which we left in the corpus for the sake of learning the (probably negative but still important) commonsense implications associated with the events. We removed a small set of truly out-dated events, but might have missed some so please email us (msap@cs.washington.edu) if you have any concerns.


### Other Known Limitations

While there are many relationships, the data is quite sparse. Also, each item of the dataset could be expanded into multiple sentences along the vsrious dimensions, oEffect, oRect, etc.

For example, given event: "PersonX uses PersonX's ___ to obtain" and dimension oReact: "annoyed", this could be transformed into an entry:

"PersonX uses PersonX's ___ to obtain => PersonY is annoyed"

## Additional Information

### Dataset Curators

The authors of Aotmic at The University of Washington

### Licensing Information

The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/

### Citation Information

@article{Sap2019ATOMICAA,
  title={ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning},
  author={Maarten Sap and Ronan Le Bras and Emily Allaway and Chandra Bhagavatula and Nicholas Lourie and Hannah Rashkin and Brendan Roof and Noah A. Smith and Yejin Choi},
  journal={ArXiv},
  year={2019},
  volume={abs/1811.00146}
}

### Contributions

Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset.