Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
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](https://huggingface.co/datasets/nkazi/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](https://aclanthology.org/S13-2045) | |
## Loading Dataset | |
```python | |
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 | |
```python | |
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 | |
```python | |
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: | |
```python | |
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: | |
```python | |
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: | |
```python | |
from datasets import DatasetDict | |
dataset = DatasetDict.load_from_disk('SciEntsBank_3way') | |
``` | |
### Printing Label Distribution | |
Use the following code to print the label distribution: | |
```python | |
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 | |
<style> | |
.label-dist th:not(:first-child), .label-dist td:not(:first-child) { | |
width: 15%; | |
} | |
</style> | |
<div class="label-dist"> | |
### 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 | |
</div> | |
## Citation | |
```tex | |
@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 | |
1. 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. |