Datasets:
pretty_name: SciEntsBank
license: cc-by-4.0
language:
- en
task_categories:
- text-classification
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: reference_answer
dtype: string
- name: student_answer
dtype: string
- name: label
dtype:
class_label:
names:
'0': correct
'1': contradictory
'2': partially_correct_incomplete
'3': irrelevant
'4': non_domain
splits:
- name: train
num_bytes: 232655
num_examples: 4969
- name: test_ua
num_bytes: 52730
num_examples: 540
- name: test_uq
num_bytes: 35716
num_examples: 733
- name: test_ud
num_bytes: 177307
num_examples: 4562
dataset_size: 498408
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test_ua
path: data/test-ua-*
- split: test_uq
path: data/test-uq-*
- split: test_ud
path: data/test-ud-*
Dataset Card for "SciEntsBank"
SciEntsBank is one of the two distinct subsets within the Student Response Analysis (SRA) corpus, the other subset being the
Beetle dataset. Derived from student answers gathered by Nielsen et al. [1],
this dataset comprises nearly 11K responses to 197 assessment questions spanning 15 diverse science domains. The dataset
features three labeling schemes: (a) 5-way, (b) 3-way, and (c) 2-way. The dataset includes a training set and three distinct
test sets: (a) Unseen Answers (test_ua), (b) Unseen Questions (test_uq), and (c) Unseen Domains (test_ud).
- Authors: Myroslava Dzikovska, Rodney Nielsen, Chris Brew, Claudia Leacock, Danilo Giampiccolo, Luisa Bentivogli, Peter Clark, Ido Dagan, Hoa Trang Dang
- Paper: SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge
Loading Dataset
from datasets import load_dataset
dataset = load_dataset('nkazi/SciEntsBank')
Labeling Schemes
The authors released the dataset with annotations using five labels (i.e., 5-way labeling scheme) for Automated Short-Answer Grading (ASAG). Additionally, the authors have introduced two alternative labeling schemes, namely the 3-way and 2-way schemes, both derived from the 5-way labeling scheme designed for Recognizing Textual Entailment (RTE). In the 3-way labeling scheme, the categories "partially correct but incomplete", "irrelevant", and "non-domain" are consolidated into a unified category labeled as "incorrect". On the other hand, the 2-way labeling scheme simplifies the classification into a binary system where all labels except "correct" are merged under the "incorrect" category.
The label column in this dataset presents the 5-way labels. For 3-way and 2-way labels, use the code provided below to derive it
from the 5-way labels. After converting the labels, please verify the label distribution. A code to print the label distribution is
also given below.
5-way to 3-way
from datasets import ClassLabel
dataset = dataset.align_labels_with_mapping({'correct': 0, 'contradictory': 1, 'partially_correct_incomplete': 2, 'irrelevant': 2, 'non_domain': 2}, 'label')
dataset = dataset.cast_column('label', ClassLabel(names=['correct', 'contradictory', 'incorrect']))
Using align_labels_with_mapping(), we are mapping "partially correct but incomplete", "irrelevant", and "non-domain" to the same id. Subsequently,
we are using cast_column() to redefine the class labels (i.e., the label feature) where the id 2 corresponds to the "incorrect" label.
5-way to 2-way
from datasets import ClassLabel
dataset = dataset.align_labels_with_mapping({'correct': 0, 'contradictory': 1, 'partially_correct_incomplete': 1, 'irrelevant': 1, 'non_domain': 1}, 'label')
dataset = dataset.cast_column('label', ClassLabel(names=['correct', 'incorrect']))
In the above code, the label "correct" is mapped to 0 to maintain consistency with both the 5-way and 3-way labeling schemes. If the preference is to represent "correct" with id 1 and "incorrect" with id 0, either adjust the label map accordingly or run the following to switch the ids:
dataset = dataset.align_labels_with_mapping({'incorrect': 0, 'correct': 1}, 'label')
Saving and loading 3-way and 2-way datasets
Use the following code to store the dataset with the 3-way (or 2-way) labeling scheme locally to eliminate the need to convert labels each time the dataset is loaded:
dataset.save_to_disk('SciEntsBank_3way')
Here, SciEntsBank_3way depicts the path/directory where the dataset will be stored. Use the following code to load the dataset from the same local directory/path:
from datasets import DatasetDict
dataset = DatasetDict.load_from_disk('SciEntsBank_3way')
Printing Label Distribution
Use the following code to print the label distribution:
def print_label_dist(dataset):
for split_name in dataset:
print(split_name, ':')
num_examples = 0
for label in dataset[split_name].features['label'].names:
count = dataset[split_name]['label'].count(dataset[split_name].features['label'].str2int(label))
print(' ', label, ':', count)
num_examples += count
print(' total :', num_examples)
print_label_dist(dataset)
Label Distribution
5-way
| Label | Train | Test UA | Test UQ | Test UD |
|---|---|---|---|---|
| Correct | 2,008 | 233 | 301 | 1,917 |
| Contradictory | 499 | 58 | 64 | 417 |
| Partially correct but incomplete | 1,324 | 113 | 175 | 986 |
| Irrelevant | 1,115 | 133 | 193 | 1,222 |
| Non-domain | 23 | 3 | - | 20 |
| Total | 4,969 | 540 | 733 | 4,562 |
3-way
| Label | Train | Test UA | Test UQ | Test UD |
|---|---|---|---|---|
| Correct | 2,008 | 233 | 301 | 1,917 |
| Contradictory | 499 | 58 | 64 | 417 |
| Incorrect | 2,462 | 249 | 368 | 2,228 |
| Total | 4,969 | 540 | 733 | 4,562 |
2-way
| Label | Train | Test UA | Test UQ | Test UD |
|---|---|---|---|---|
| Correct | 2,008 | 233 | 301 | 1,917 |
| Incorrect | 2,961 | 307 | 432 | 2,645 |
| Total | 4,969 | 540 | 733 | 4,562 |
Citation
Please consider adding a footnote linking to this dataset page (e.g., SciEntsBank\footnote{https://huggingface.co/datasets/nkazi/SciEntsBank} in LaTeX)
when first mentioning the dataset in your paper, alongside citing the authors/paper. This will promote the availability of this dataset on
Hugging Face and make it more accessible to researchers, given that the original repository is no longer available.
@inproceedings{dzikovska2013semeval,
title = {{S}em{E}val-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge},
author = {Dzikovska, Myroslava and Nielsen, Rodney and Brew, Chris and Leacock, Claudia and Giampiccolo, Danilo and Bentivogli, Luisa and Clark, Peter and Dagan, Ido and Dang, Hoa Trang},
year = 2013,
month = jun,
booktitle = {Second Joint Conference on Lexical and Computational Semantics ({SEM}), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation ({S}em{E}val 2013)},
editor = {Manandhar, Suresh and Yuret, Deniz}
publisher = {Association for Computational Linguistics},
address = {Atlanta, Georgia, USA},
pages = {263--274},
url = {https://aclanthology.org/S13-2045},
}
References
- Rodney D. Nielsen, Wayne Ward, James H. Martin, and Martha Palmer. 2008. Annotating students' understanding of science concepts. In Proceedings of the Sixth International Language Resources and Evaluation Conference, Marrakech, Morocco.