Datasets:

neulab
/

merlin

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 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:

{
  "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

{
  "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.

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.

Related Work

This dataset can be benchmarked with:

mGENRE (Multilingual Generative Entity Retrieval) Repo
GEMEL (Generative Multimodal Entity Linking) Repo