agentlans/en-translations

Multilingual Parallel Sentences with Semantic Similarity Scores and Quality Metrics

This dataset is a diverse collection of parallel sentences in English and various other languages, sourced from multiple high-quality datasets. Each sentence pair includes a semantic similarity score calculated using the Language-agnostic BERT Sentence Embedding (LaBSE) model, along with additional quality metrics.

Supported Tasks

This dataset supports:

• Machine Translation
• Cross-lingual Semantic Similarity
• Multilingual Natural Language Understanding
• Translation Quality Estimation

Languages

The dataset includes English paired with multiple languages from sources such as JW300, Europarl, TED Talks, OPUS-100, Tatoeba, Global Voices, and News Commentary.

Please see sentence-transformers/parallel-sentences-datasets for the original sources.

Dataset Structure

Data Instances

Each instance contains:

• english: The English sentence (string)
• non_english: The corresponding sentence in another language (string)
• distance: Semantic similarity score (cosine distance) between the sentences (float)
• quality: Content quality score (float)
• readability: Readability score (float)
• sentiment: Sentiment score (float)

Example:

{
  "english": "If we start to think exponentially, we can see how this is starting to affect all the technologies around us.",
  "non_english": "Če začnemo misliti eksponentno, vidimo, kako to začenja vplivati ​​na vse tehnologije okoli nas.",
  "distance": 0.05299,
  "quality": 0.3359375,
  "readability": 0.103515625,
  "sentiment": 0.45703125
}

Data Splits

The dataset is divided into:

• Train: 867 042 rows (90%)
• Validation: 96 338 rows (10%)
• Total: 963 380 rows (100%)

Dataset Creation

Sentences were downloaded from different splits and configurations of each dataset, ensuring a rich variety of linguistic representations. To ensure high quality, the dataset was deduplicated, and only sentence pairs with a semantic similarity score (distance) below 0.25 were included. A total of 5 000 sentences were downloaded from each split of each dataset, resulting in a final distribution of 90% training and 10% validation.

Annotations

Semantic similarity scores were generated using the LaBSE model by calculating cosine distances between embeddings. Additional metrics were annotated using the quality, readability, and sentiment models.

Considerations for Using the Data

Social Impact

This dataset can enhance cross-lingual NLP models and applications by providing high-quality parallel sentences with semantic similarity and quality metrics.

Known Limitations

• The semantic similarity (distance) and quality scores may not capture all nuances of cross-lingual similarity or translation quality.
• Coverage is limited to languages present in the source datasets.
• Filtering based on distance < 0.25 may exclude some valid but less similar translations.