---
dataset_info:
  features:
  - name: query
    dtype: string
  - name: positive
    dtype: string
  - name: hard_negative
    dtype: string
  - name: negative
    dtype: string
  - name: type
    dtype: string
  - name: lang_query
    dtype: string
  - name: lang_positive
    dtype: string
  - name: lang_hard_negative
    dtype: string
  - name: lang_negative
    dtype: string
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 104365393
    num_examples: 40118
  - name: validation
    num_bytes: 13071951
    num_examples: 5015
  - name: test
    num_bytes: 13113573
    num_examples: 5015
  download_size: 72943484
  dataset_size: 130550917
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

# 📚 Multilingual Scientific Triplet Dataset (EN–ES–CA)

A high-quality **50,148-triplet** multilingual dataset for fine-tuning **Sentence Transformers** and other contrastive embedding models.

Designed specifically for **scientific document retrieval**, **semantic search**, and **cross-lingual embedding alignment** across three languages:

- **English (EN)**
- **Spanish (ES)**
- **Catalan (CA)**

---

## 🔬 Dataset Summary

This dataset contains triplets of the form:

```
(query, positive, hard_negative, negative)
```

constructed from scientific **titles**, **abstracts**, and **title+abstract combinations**.

Composition:

- **25,074 monolingual triplets**
- **25,074 cross-lingual mixed triplets**
- **Balanced across EN / ES / CA**
- **Includes hard negatives**
- Ideal for `MultipleNegativesRankingLoss` training

## 🔧 Dataset Construction Methodology

This dataset is built using the **Microsoft Academic Graph (MAG)** *Fields of Study (FoS)* **Level-0 categories**, which are the most fine-grained scientific topics in the MAG hierarchy.

Examples of Level-0 FoS include:

- *Accounting*
- *Aeronautics*
- *Artificial intelligence*
- *Biochemistry*
- *Computer vision*
- *Cognitive psychology*
- *Civil engineering*
- *Control theory*
- *Dermatology*
- *Econometrics*
- *Environmental chemistry*
- *Data mining*
- *Criminology*
- *Endocrinology*
- … and **hundreds more**

All Level-0 FoS names were translated into **Catalan** and **Spanish** using Google Translate to enable multilingual queries.

## 🔹 Query Construction (MAG Level-1 FoS Labels)

The **query** is always a **MAG FoS Level-1 field name**, in one of the three languages (EN, ES, CA):

Example:
- `"Artificial intelligence"`  
- `"Inteligencia artificial"`  
- `"Intel·ligència artificial"`

This ensures that queries represent precise scientific topics.

---

## 🔹 Positive Construction (Same Level-1 FoS)

The **positive** passage is:

- A scientific abstract (or title/abstract)
- Labeled with the **exact same Level-1 FoS** as the query

This provides strong semantic similarity.

### Example
Query (Level-1 FoS): *Biochemistry*  
Positive: Abstract from a paper labeled *Biochemistry*

---

## 🔸 Hard Negative Construction (Different Level-1, Same Level-0 Parent)

MAG organizes Level-0 FoS into **parent Level-0 disciplines**.

A **hard negative** is:

- From a **different Level-1 FoS**
- But with the **same Level-0 parent** discipline

This makes it:
- *Domain-related* → challenging  
- *Incorrect* → good for contrastive separation  

### Example
- **Level-0 parent:** Chemistry  
  - Level-1 FoS: *Biochemistry* → **query/positive**
  - Level-1 FoS: *Chromatography* → **hard negative**
  - (Same parent → Chemistry)

This ensures meaningful hard negatives.

---

## 🔹 Negative Construction (Different Parent Discipline)

A **negative** passage is:

- From a Level-0 FoS whose **Level-0 parent** is **different** from the query’s parent discipline  
- Farther in the taxonomy → easier negative

### Example

- Query Level-0: *Chemistry*
- Negative Level-0: *Psychology* or *Computer Science*

This encourages strong contrast and prevents semantic drift.

---

## 🧪 Summary of Sampling Rules

| Component        | MAG FoS Relationship to Query | Purpose |
|------------------|-------------------------------|---------|
| **Query**        | Level-1 FoS name              | Topic anchor |
| **Positive**     | Same Level-1 FoS              | True semantic match |
| **Hard Negative**| Different Level-1, **same Level-0 parent** | Challenging distractor |
| **Negative**     | Different Level-0 parent      | Domain-distant negative |

This hierarchical sampling approach provides:

- High-quality contrastive supervision  
- True scientific domain understanding  
- Strong multilingual & cross-lingual alignment  
- Better document retrieval performance  

---

## 🌐 Multilingual Expansion

- All Level-1 FoS names originally in English were translated into:
  - **Catalan**
  - **Spanish**
- Abstracts (passages) remain in the original language of the paper
- Thus, queries are multilingual while scientific content remains realistic

This ensures:
- High-quality multilingual query formulation
- Real-world cross-lingual retrieval scenarios
- Balanced EN/ES/CA distributions

---

## 🧩 Data Fields

| Column | Description |
|--------|-------------|
| `query` | Query text (title, abstract, or combined) |
| `positive` | Semantically relevant passage |
| `hard_negative` | Hard negative passage from similar domain |
| `negative` | Distant, semantically unrelated passage |
| `lang_query` | Language of query (`en`, `es`, `ca`) |
| `lang_positive` | Language of positive passage |
| `lang_hard_negative` | Language of hard negative |
| `lang_negative` | Language of negative |
| `type` | `monolingual` or `crosslingual` |

---

## 🌍 Linguistic Composition

### Query Language Distribution
- **EN:** 17,012  
- **ES:** 16,592  
- **CA:** 16,544  

### Passage Language Balance
All passage types (positive, hard negative, negative) are **evenly balanced** across EN/ES/CA.

---

## 🔁 Monolingual vs Cross-Lingual Structure

### **50% Monolingual Triplets**
- Query and all passages share the same language.
- Even distribution: EN/ES/CA (~8.3k each).
- Reinforces strong **intra-language semantic learning**.

### **50% Cross-Lingual Mixed Triplets**
- Query language is random.
- Passages are independently sampled from EN/ES/CA.
- **At least one passage differs in language from the query.**
- Hard negatives and negatives may be from *different* languages.
- Achieves **true multilingual alignment**, not just pairwise translation.

---

## 🎯 Intended Use-Cases

This dataset is ideal for:

- 🧠 Multilingual Sentence Transformer fine-tuning  
- 🌐 Cross-lingual scientific document retrieval  
- 🔎 Semantic search engines  
- 📚 Scientific knowledge embedding  
- 🤖 Zero-shot multilingual retrieval  
- 🧬 Domain-specific embedding learning  

---

## 📈 Example Triplet

```json
{
  "query": "Química física",
  "positive": "Presentamos una investigación teórica...",
  "hard_negative": "Estudi computacional de sistemes catalítics...",
  "negative": "Adaptive control for dynamic systems...",
  "lang_query": "es",
  "lang_positive": "es",
  "lang_hard_negative": "ca",
  "lang_negative": "en",
  "type": "crosslingual"
}
```

## 🧮 Dataset Statistics

- **Total samples:** 50,148  
- **Monolingual:** 25,074  
- **Crosslingual:** 25,074  

### Passage Lengths (tokens)

| Field          | Mean   |
|----------------|--------|
| Query          | 1.82   |
| Positive       | 126.3  |
| Hard Negative  | 108.9  |
| Negative       | 136.1  |

---

## 🧪 Dataset Splits

Pre-defined splits:

| Split       | Size            |
|-------------|------------------|
| **train**     | 40,118 (80%)     |
| **validation**| 5,015 (10%)      |
| **test**      | 5,015 (10%)      |