--- 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: ```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": "" } ``` ### 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": "" } ``` ### 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)