---
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.",
}
```