license:
- cc-by-4.0
multilinguality:
- monolingual
- aligned
task_categories:
- text-classification
- text2text-generation
source_datasets:
- original
- >-
extended|other-turkcorpus,other-asset,other-questeval,other-simplicity_da,other-simp_da
language:
- en
tags:
- simplification-evaluation
- meaning-evaluation
pretty_name: CSMD
size_categories:
- 1K<n<10K
dataset_info:
- config_name: meaning
features:
- name: document
dtype: string
- name: simplification
dtype: string
- name: labels
dtype: int
splits:
- name: train
num_bytes: 251558
num_examples: 853
- name: dev
num_bytes: 27794
num_examples: 95
- name: test
num_bytes: 117686
num_examples: 407
download_size: 397038
dataset_size: 1355
- config_name: meaning_with_data_augmentation
features:
- name: document
dtype: string
- name: simplification
dtype: string
- name: labels
dtype: int
splits:
- name: train
num_bytes: 1151604
num_examples: 2560
- name: dev
num_bytes: 120991
num_examples: 285
- name: test
num_bytes: 540844
num_examples: 1220
download_size: 1813439
dataset_size: 4065
- config_name: meaning_holdout_identical
features:
- name: document
dtype: string
- name: simplification
dtype: string
- name: labels
dtype: int
splits:
- name: test
num_bytes: 89866
num_examples: 359
download_size: 89866
dataset_size: 359
- config_name: meaning_holdout_unrelated
features:
- name: document
dtype: string
- name: simplification
dtype: string
- name: labels
dtype: int
splits:
- name: test
num_bytes: 247835
num_examples: 359
download_size: 247835
dataset_size: 359
config_names:
- meaning
- meaning_with_data_augmentation
- meaning_holdout_identical
- meaning_holdout_unrelated
viewer: true
Dataset Card for "Continuous Scale Meaning Dataset" (CSMD)
CSMD was created for MeaningBERT: Assessing Meaning Preservation Between Sentences.
It contains 1,355 English text simplification meaning preservation annotations. Meaning preservation measures how well the meaning of the output text corresponds to the meaning of the source (Saggion, 2017).
The annotations were taken from the following four datasets:
- ASSET
- QuestEVal,
- SimpDa_2022 and,
- Simplicity-DA.
It contains a data augmentation subset of 1,355 identical sentence triplets and 1,355 unrelated sentence triplets (See the "Sanity Checks" section (3.3.) in our article).
It also contains two holdout subsets of 359 identical sentence triplets and 359 unrelated sentence triples (See the "MeaningBERT" section (3.4.) in our article).
Dataset Structure
Data Instances
Meaning
configuration: an instance consists of 1,355 meaning preservation triplets (Document, simplification, label).meaning_with_data_augmentation
configuration: an instance consists of 1,355 meaning preservation triplets (Document, simplification, label) along with 1,355 data augmentation triplets (Document, Document, 1) and 1,355 data augmentation triplets (Document, Unrelated Document, 0) (See the sanity checks in our article).meaning_holdout_identical
configuration: an instance consists of 359 meaning holdout preservation identical triplets (Document, Document, 1) based on the ASSET Simplification dataset.meaning_holdout_unrelated
configuration: an instance consists of 359 meaning holdout preservation unrelated triplets (Document, Unrelated Document, 0) based on the ASSET Simplification dataset.
Data Fields
document
: an original sentence from the source datasets.simplification
: a simplification of the original obtained by an automated system or a human.labels
: a meaning preservation rating between 0 and 100.
Data Splits
The split statistics of CSMD are given below.
Train | Dev | Test | Total | |
---|---|---|---|---|
Meaning | 853 | 95 | 407 | 1,355 |
Meaning With Data Augmentation | 2,560 | 285 | 1,220 | 4,065 |
Meaning Holdout Identical | NA | NA | 359 | 359 |
Meaning Holdout Unrelated | NA | NA | 359 | 359 |
All the splits are randomly split using a 60-10-30 split with the seed 42
.
Citation Information
@ARTICLE{10.3389/frai.2023.1223924,
AUTHOR={Beauchemin, David and Saggion, Horacio and Khoury, Richard},
TITLE={{MeaningBERT: Assessing Meaning Preservation Between Sentences}},
JOURNAL={Frontiers in Artificial Intelligence},
VOLUME={6},
YEAR={2023},
URL={https://www.frontiersin.org/articles/10.3389/frai.2023.1223924},
DOI={10.3389/frai.2023.1223924},
ISSN={2624-8212},
}