Update README.md

README.md

@@ -1,233 +1,130 @@
----
-dataset_info:
-- config_name: hi
-  features:
-  - name: Article Title
-    dtype: string
-  - name: Entity Name
-    dtype: string
-  - name: Wikidata ID
-    dtype: string
-  - name: English Wikipedia Title
-    dtype: string
-  - name: Image Name
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 51118097.546
-    num_examples: 1414
-  download_size: 29882467
-  dataset_size: 51118097.546
-- config_name: id
-  features:
-  - name: Article Title
-    dtype: string
-  - name: Entity Name
-    dtype: string
-  - name: Wikidata ID
-    dtype: string
-  - name: English Wikipedia Title
-    dtype: string
-  - name: Image Name
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 52546850.192
-    num_examples: 1428
-  download_size: 32136412
-  dataset_size: 52546850.192
-- config_name: ja
-  features:
-  - name: Article Title
-    dtype: string
-  - name: Entity Name
-    dtype: string
-  - name: Wikidata ID
-    dtype: string
-  - name: English Wikipedia Title
-    dtype: string
-  - name: Image Name
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 62643647.72
-    num_examples: 1720
-  download_size: 35163853
-  dataset_size: 62643647.72
-- config_name: ta
-  features:
-  - name: Article Title
-    dtype: string
-  - name: Entity Name
-    dtype: string
-  - name: Wikidata ID
-    dtype: string
-  - name: English Wikipedia Title
-    dtype: string
-  - name: Image Name
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 44337774.542
-    num_examples: 1254
-  download_size: 30111872
-  dataset_size: 44337774.542
-- config_name: vi
-  features:
-  - name: Article Title
-    dtype: string
-  - name: Entity Name
-    dtype: string
-  - name: Wikidata ID
-    dtype: string
-  - name: English Wikipedia Title
-    dtype: string
-  - name: Image Name
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 46272154.251
-    num_examples: 1343
-  download_size: 27669139
-  dataset_size: 46272154.251
-configs:
-- config_name: hi
-  data_files:
-  - split: train
-    path: hi/train-*
-- config_name: id
-  data_files:
-  - split: train
-    path: id/train-*
-- config_name: ja
-  data_files:
-  - split: train
-    path: ja/train-*
-- config_name: ta
-  data_files:
-  - split: train
-    path: ta/train-*
-- config_name: vi
-  data_files:
-  - split: train
-    path: vi/train-*
----
-
-# MERLIN Dataset
+# 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. The dataset contains news articles in multiple languages along with associated images and entity annotations.
+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
-- Cross-lingual Entity Linking
-- Named Entity Recognition
+- **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
-- [Add other languages present in your dataset]
+- Hindi  
+- Japanese  
+- Indonesian  
+- Vietnamese  
+- Tamil
 
 ### Data Instances
 Each instance in the dataset contains:
-```python
+
+```json
 {
-    "Article Title": "बिहार: केंद्रीय मंत्री अश्विनी चौबे के बेटे अर्जित 'गिरफ्तार'",  # Article title in original language
-    "Entity Name": "अश्विनी चौबे",                # Entity mention in original language
-    "Wikidata ID": "Q16728021",                   # Unique Wikidata identifier
-    "English Wikipedia Title": "Ashwini Kumar Choubey",  # Corresponding English Wikipedia title
-    "Image Name": "<GCS_URL>"                     # URL to associated image
+  "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`: The title of the news article in its original language
-- `Entity_Name`: The name of the entity to be linked in its original language
-- `Wikidata_ID`: The corresponding Wikidata identifier for the entity
-- `English_Wikipedia_Title`: The English Wikipedia page title for the entity
-- `image`: Associated image URL from Google Cloud Storage
+- **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 [number] examples.
+- The dataset contains **only a test split**, with **5,000 article titles** (1,000 per language).
+
+---
 
 ## Dataset Creation
 
 ### Source Data
-[Describe where the data comes from, e.g., news sources, etc.]
+- 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
-[Describe how entities were annotated/linked]
+- **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
 
-### Data Instances
-Example of a data instance:
-```python
+### 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>"
+  "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`: string
-- `Entity_Name`: string
-- `Wikidata_ID`: string
-- `English_Wikipedia_Title`: string
-- `image`: binary image data
-
 ### Data Statistics
-- Total number of instances: [number]
-- Number of unique entities: [number]
-- Languages covered: [list of languages]
-- [Any other relevant 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)  
 
-## Dataset Creation
-
-### Curation Rationale
-[Explain why this dataset was created]
+---
 
-### Source Data
-[Describe the source of the articles and images]
+## 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**.
 
-### Annotations
-[Describe the annotation process if any]
+---
 
 ## Considerations for Using the Data
 
-### Social Impact of Dataset
-[Describe potential social impacts]
+### 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
-[Discuss any potential biases in the data]
+- 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
-[Describe any 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
-[Your name/organization]
+- Carnegie Mellon University (CMU)  
+- Defence Science and Technology Agency, Singapore  
 
 ### Licensing Information
-[License information]
+- The dataset is released for **research purposes only**, under the license specified in the [GitHub repository](https://github.com/rsathya4802/merlin).  
 
 ### Citation Information
-```bibtex
-[Citation when available]
-```
+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
-[How others can contribute to the dataset]
+Community contributions can be made via the [MERLIN GitHub repo](https://github.com/rsathya4802/merlin).  
 
-## Related Work
-This dataset can be used with the following baseline models:
-1. GEMEL (Generative Multimodal Entity Linking)
-2. GENRE (Generative ENtity REtrieval)
+---
 
-For more information about these baselines, visit: [Your repository URL]
-```
+## 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)