---
dataset_info:
  features:
  - name: word
    dtype: string
  - name: avg_google_ngrams_frequency
    dtype: float64
  splits:
  - name: ar
    num_bytes: 7869
    num_examples: 308
  - name: en
    num_bytes: 6190
    num_examples: 334
  - name: fr
    num_bytes: 7469
    num_examples: 380
  - name: ru
    num_bytes: 8948
    num_examples: 330
  - name: zh
    num_bytes: 4683
    num_examples: 254
  download_size: 33823
  dataset_size: 35159
configs:
- config_name: default
  data_files:
  - split: ar
    path: data/ar-*
  - split: en
    path: data/en-*
  - split: fr
    path: data/fr-*
  - split: ru
    path: data/ru-*
  - split: zh
    path: data/zh-*
language:
- ar
- en
- fr
- ru
- zh
pretty_name: Multilingual Word-in-Context Homonyms
size_categories:
- 1K<n<10K
license: cc-by-nc-sa-4.0
---

# Multilingual Word-in-Context Homonyms (ML-WiC)

**Dataset Author**: Lukas Ellinger  
**Original Source**: Adapted from [Martelli et al. (2021)](https://aclanthology.org/2021.semeval-1.3/)    
**License**: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)  
**Language**: Arabic, English, French, Russian, Simplified Chinese  
**Size**: 1606 examples
**Task**: Word Sense Disambiguation (WSD), Definition Generation

---

## Dataset Summary
Each entry includes:

- A **homonym**
- Its **average frequency** in Google N-grams

---

## Dataset Construction
The ML-WiC dataset was constructed to target homonyms with multiple **distinct senses**.
The dataset from [Martelli et al. (2021)](https://aclanthology.org/2021.semeval-1.3/) was designed for Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC). This task 
involves determining whether a target word retains the same sense in two sentences within and across languages. We filtered the dataset for words with
distinct senses in the same language, identifying them thus as homonyms.

---

## Citation

If you use any of the work, please cite the following paper:

```tex
@InProceedings{ellinger-anschtz-groh:2025:RANLP,
  author    = {Ellinger, Lukas  and  Anschütz, Miriam  and  Groh, Georg},
  title     = {Simplifications Are Absolutists: How Simplified Language Reduces Word Sense Awareness in LLM-Generated Definitions},
  booktitle      = {Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI era},
  month          = {September},
  year           = {2025},
  address        = {Varna, Bulgaria},
  publisher      = {INCOMA Ltd., Shoumen, Bulgaria},
  pages     = {342--351},
  abstract  = {Large Language Models (LLMs) can provide accurate word definitions and explanations for any context. However, the scope of the definition changes for different target groups, like children or language learners. This is especially relevant for homonyms—words with multiple meanings—where oversimplification might risk information loss by omitting key senses, potentially misleading users who trust LLM outputs. We investigate how simplification impacts homonym definition quality across three target groups: Normal, Simple, and ELI5. Using two novel evaluation datasets spanning multiple languages, we test DeepSeek v3, Llama 4 Maverick, Qwen3-30B A3B, GPT-4o mini, and Llama 3.1 8B via LLM-as-Judge and human annotations. Our results show that simplification drastically degrades definition completeness by neglecting polysemy, increasing the risk of misunderstanding. Fine-tuning Llama 3.1 8B with Direct Preference Optimization substantially improves homonym response quality across all prompt types. These findings highlight the need to balance simplicity and completeness in educational NLP to ensure reliable, context-aware definitions for all learners.},
  url       = {https://aclanthology.org/2025.ranlp-1.42}
}
```