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---
language:
- deu
- eng
- spa
- fra
multilinguality:
- multilingual
configs:
- config_name: Norm_Dup
data_files:
- split: train
path: "data/train/trainset_norm_dup.csv"
- split: test
path: "data/test/testset_norm_dup.csv"
- config_name: Norm_Dedup
data_files:
- split: train
path: "data/train/trainset_norm_dedup.csv"
- split: test
path: "data/test/testset_norm_dedup.csv"
- config_name: Proc_Dup
data_files:
- split: train
path: "data/train/trainset_proc_dup.csv"
- split: test
path: "data/test/testset_proc_dup.csv"
- config_name: Proc_Dedup
data_files:
- split: train
path: "data/train/trainset_proc_dup.csv"
- split: test
path: "data/test/testset_proc_dup.csv"
---
> [!NOTE]
> Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/9487
# Mulve
Multi-Language Vocabulary Evaluation Data Set (MuLVE) is a data set consisting of vocabulary cards and real-life user answers, labeled whether the user answer is correct or incorrect. The data's source is user learning data from the Phase6 vocabulary trainer. The data set contains vocabulary questions in German and English, Spanish, and French as target language and is available in four different variations regarding pre-processing and deduplication.
It is split up into four tab-separated files, one for each variation, per train and test set. The files include the following columns:
cardId - numeric card ID
question - volcabulary card question
answer - volcabulary card answer
userAnswer - aragswer the user input
Label - turue if user answer is correct, False if not
language - tamrget language (English, French or Spanish)
The processed data set variations do not include the include \textbf{userAnswer} columns but the following additional columns:
question_norm - queestion normalized
answer_norm - aragswer normalized
userAnswer_norm - user answer normalized
# Reference
```
@inproceedings{jacobsen-etal-2022-mulve,
title = "{M}u{LVE}, A Multi-Language Vocabulary Evaluation Data Set",
author = {Jacobsen, Anik and
Mohtaj, Salar and
M{\"o}ller, Sebastian},
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.70",
pages = "673--679",
abstract = "Vocabulary learning is vital to foreign language learning. Correct and adequate feedback is essential to successful and satisfying vocabulary training. However, many vocabulary and language evaluation systems perform on simple rules and do not account for real-life user learning data. This work introduces Multi-Language Vocabulary Evaluation Data Set (MuLVE), a data set consisting of vocabulary cards and real-life user answers, labeled indicating whether the user answer is correct or incorrect. The data source is user learning data from the Phase6 vocabulary trainer. The data set contains vocabulary questions in German and English, Spanish, and French as target language and is available in four different variations regarding pre-processing and deduplication. We experiment to fine-tune pre-trained BERT language models on the downstream task of vocabulary evaluation with the proposed MuLVE data set. The results provide outstanding results of {\textgreater} 95.5 accuracy and F2-score. The data set is available on the European Language Grid.",
}
``` |