README.md

---
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---

# MERLIN Dataset Card

## Dataset Description

### Overview
MERLIN (Multilingual Entity Recognition and Linking) is a test dataset for evaluating multilingual entity linking systems with multimodal inputs. It consists of **BBC news article titles** in multiple languages, each paired with an associated image and entity annotations. The dataset contains **7,287 entity mentions** linked to **2,480 unique Wikidata entities**, covering a wide range of categories (persons, locations, organizations, events, etc.).

### Supported Tasks
- **Multimodal Entity Linking** – disambiguating entity mentions using both text and images.  
- **Cross-lingual Entity Linking** – linking mentions in one language to Wikidata entities regardless of language.  
- **Named Entity Recognition** – identifying entity mentions in non-English news titles.  

### Languages
- Hindi  
- Japanese  
- Indonesian  
- Vietnamese  
- Tamil

### Data Instances
Each instance in the dataset contains:

```json
{
  "Article_Title": "बिहार: केंद्रीय मंत्री अश्विनी चौबे के बेटे अर्जित 'गिरफ्तार'",
  "Entity_Name": "अश्विनी चौबे",
  "Wikidata_ID": "Q16728021",
  "English_Wikipedia_Title": "Ashwini Kumar Choubey",
  "Image_Name": "<GCS_URL>"
}
```

### Data Fields
- **Article_Title**: News article title in its original language (string)  
- **Entity_Name**: Entity mention in the same language (string)  
- **Wikidata_ID**: Wikidata identifier for the entity (string)  
- **English_Wikipedia_Title**: English Wikipedia page title (string)  
- **Image_Name**: Associated image filename/URL (string)  

### Data Splits
- The dataset contains **only a test split**, with **5,000 article titles** (1,000 per language).

---

## Dataset Creation

### Source Data
- Derived from the **M3LS dataset** (Verma et al., 2023), which was curated from **BBC News articles** spanning over a decade.  
- Articles include categories like politics, sports, economy, science, and technology.  
- Each article includes a **headline and an associated image**.

### Annotations
- **Tool used**: INCEpTION annotation platform.  
- **Knowledge base**: Wikidata.  
- **Process**:  
  - Annotators highlighted entity mentions in article titles and linked them to Wikidata entries.  
  - Each title was annotated by **three annotators**, with **majority voting** used for final selection.  
  - Annotators were recruited via **Prolific** with prescreening (required F1 ≥ 60% on English pilot tasks).  
- **Agreement**: Average inter-annotator Cohen’s Kappa ≈ **0.83** (almost perfect agreement).  

---

## Dataset Structure

### Example
```json
{
  "Article_Title": "बिहार: केंद्रीय मंत्री अश्विनी चौबे के बेटे अर्जित 'गिरफ्तार'",
  "Entity_Name": "अश्विनी चौबे",
  "Wikidata_ID": "Q16728021",
  "English_Wikipedia_Title": "Ashwini Kumar Choubey",
  "Image_Name": "<GCS_URL>"
}
```

### Data Statistics
- **Total article titles**: 5,000  
- **Total mentions**: 7,287  
- **Unique entities**: 2,480  
- **Languages covered**: Hindi, Japanese, Indonesian, Tamil, Vietnamese  
- **Avg. words per title**: ~11.1  
- **Unlinked mentions**: 1,243 (excluded from benchmark tasks)  

---

## Curation Rationale
MERLIN was created to provide the **first multilingual multimodal entity linking benchmark**, addressing the gap where existing datasets are either monolingual or text-only. It enables studying how images can resolve ambiguity in entity mentions, especially in **low-resource languages**.

---

## Considerations for Using the Data

### Social Impact
- Supports **fairer multilingual NLP research**, by including low-resource languages (Tamil, Vietnamese).  
- Encourages development of models robust to both text and images.  

### Discussion of Biases
- All data is from **BBC News**, limiting genre diversity.  
- Annotators’ **background knowledge** and **language proficiency** may introduce subtle biases.  
- **Wikidata coverage bias**: entities absent from Wikidata were excluded (≈17% of mentions unlinked).  

### Other Known Limitations
- Domain restriction (news only).  
- Focused on entity mentions in headlines, not longer text.  
- Baseline methods link to **Wikipedia titles** rather than pure **Wikidata QIDs**.  

---

## Additional Information

### Dataset Curators
- Carnegie Mellon University (CMU)  
- Defence Science and Technology Agency, Singapore  

### Licensing Information
- The dataset is released for **research purposes only**, under the license specified in the [GitHub repository](https://github.com/rsathya4802/merlin).  

### Citation Information
If you use MERLIN, cite:  
**Ramamoorthy, S., Shah, V., Khanuja, S., Sheikh, Z., Jie, S., Chia, A., Chua, S., & Neubig, G. (2025). MERLIN: A Testbed for Multilingual Multimodal Entity Recognition and Linking. Transactions of the Association for Computational Linguistics.**  

### Contributions
Community contributions can be made via the [MERLIN GitHub repo](https://github.com/rsathya4802/merlin).  

---

## Related Work
This dataset can be benchmarked with:  
- **mGENRE** (Multilingual Generative Entity Retrieval) [Repo](https://huggingface.co/facebook/mgenre-wiki)
- **GEMEL** (Generative Multimodal Entity Linking) [Repo](https://github.com/HITsz-TMG/GEMEL)