holylovenia
commited on
Commit
•
c9ee765
1
Parent(s):
2e795b9
Upload README.md with huggingface_hub
Browse files
README.md
ADDED
@@ -0,0 +1,53 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: unknown
|
3 |
+
tags:
|
4 |
+
- short-answer-grading
|
5 |
+
language:
|
6 |
+
- ind
|
7 |
+
---
|
8 |
+
|
9 |
+
# id_short_answer_grading
|
10 |
+
|
11 |
+
Indonesian short answers for Biology and Geography subjects from 534 respondents where the answer grading was done by 7 experts.
|
12 |
+
|
13 |
+
## Dataset Usage
|
14 |
+
|
15 |
+
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
|
16 |
+
|
17 |
+
## Citation
|
18 |
+
|
19 |
+
```
|
20 |
+
@article{
|
21 |
+
JLK,
|
22 |
+
author = {Muh Haidir and Ayu Purwarianti},
|
23 |
+
title = { Short Answer Grading Using Contextual Word Embedding and Linear Regression},
|
24 |
+
journal = {Jurnal Linguistik Komputasional},
|
25 |
+
volume = {3},
|
26 |
+
number = {2},
|
27 |
+
year = {2020},
|
28 |
+
keywords = {},
|
29 |
+
abstract = {Abstract—One of the obstacles in an efficient MOOC is the evaluation of student answers, including the short answer grading which requires large effort from instructors to conduct it manually.
|
30 |
+
Thus, NLP research in short answer grading has been conducted in order to support the automation, using several techniques such as rule
|
31 |
+
and machine learning based. Here, we’ve conducted experiments on deep learning based short answer grading to compare the answer
|
32 |
+
representation and answer assessment method. In the answer representation, we compared word embedding and sentence embedding models
|
33 |
+
such as BERT, and its modification. In the answer assessment method, we use linear regression. There are 2 datasets that we used, available
|
34 |
+
English short answer grading dataset with 80 questions and 2442 to get the best configuration for model and Indonesian short answer grading
|
35 |
+
dataset with 36 questions and 9165 short answers as testing data. Here, we’ve collected Indonesian short answers for Biology and Geography
|
36 |
+
subjects from 534 respondents where the answer grading was done by 7 experts. The best root mean squared error for both dataset was achieved
|
37 |
+
by using BERT pretrained, 0.880 for English dataset dan 1.893 for Indonesian dataset.},
|
38 |
+
issn = {2621-9336}, pages = {54--61}, doi = {10.26418/jlk.v3i2.38},
|
39 |
+
url = {https://inacl.id/journal/index.php/jlk/article/view/38}
|
40 |
+
}
|
41 |
+
```
|
42 |
+
|
43 |
+
## License
|
44 |
+
|
45 |
+
Unknown
|
46 |
+
|
47 |
+
## Homepage
|
48 |
+
|
49 |
+
[https://github.com/AgeMagi/tugas-akhir](https://github.com/AgeMagi/tugas-akhir)
|
50 |
+
|
51 |
+
### NusaCatalogue
|
52 |
+
|
53 |
+
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
|