Create README.md

README.md

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+---
+language:
+- de
+- en
+- es
+- fr
+configs:
+- config_name: Norm_Dup
+  data_files:
+  - split: train
+    path: trainset_norm_dup.csv
+  - split: test
+    path: testset_norm_dup.csv
+- config_name: Norm_Dedup
+  data_files:
+  - split: train
+    path: trainset_norm_dedup.csv
+  - split: test
+    path: testset_norm_dedup.csv
+- config_name: Proc_Dup
+  data_files:
+  - split: train
+    path: trainset_proc_dup.csv
+  - split: test
+    path: testset_proc_dup.csv
+- config_name: Proc_Dedup
+  data_files:
+  - split: train
+    path: trainset_proc_dup.csv
+  - split: test
+    path: testset_proc_dup.csv
+---
+
+# 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 - vocabulary card question
+    answer - vocabulary card answer
+    userAnswer - answer the user input
+    Label - True if user answer is correct, False if not
+    language - target 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 - question normalized
+    answer_norm - answer 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.",
+}
+```