Tran1312/MachineTranslation_en_vi

English–Vietnamese Machine Translation Dataset 2025

Dataset Description

This dataset is a large-scale English–Vietnamese parallel corpus designed for training and evaluating neural machine translation systems.

The corpus was constructed by aggregating sentence pairs from several publicly available English–Vietnamese datasets and applying a multi-stage filtering pipeline to reduce malformed samples, duplicated content, language mismatches, and semantically misaligned sentence pairs.

The resulting dataset contains between 10 million and 100 million parallel sentence pairs after aggregation and filtering.

Supported Task

• Machine Translation
  • English → Vietnamese
  • Vietnamese → English

Languages

• English (en)
• Vietnamese (vi)

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Data Sources

The dataset was constructed from multiple publicly available English–Vietnamese parallel corpora:

Source	Description / Link
CCMatrix	https://opus.nlpl.eu/CCMatrix/en&vi/v1/CCMatrix
OpenSubtitles	https://opus.nlpl.eu/OpenSubtitles/en&vi/v2024/OpenSubtitles
MultiHPLT	https://opus.nlpl.eu/MultiHPLT/en&vi/v2/MultiHPLT
CCAligned	https://opus.nlpl.eu/CCAligned/en&vi/v1/CCAligned
ParaCrawl	https://opus.nlpl.eu/ParaCrawl-Bonus/en&vi/v9/ParaCrawl-Bonus
PhoMT	https://huggingface.co/datasets/ura-hcmut/PhoMT
VietAI MT	https://huggingface.co/datasets/wanhin/VietAI_MTet

These sources provide parallel text from heterogeneous domains such as web content, subtitles, crawled documents, and curated machine translation corpora.

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Data Processing Pipeline

The raw corpora were passed through several stages of rule-based and model-based filtering.

The overall pipeline can be summarized as:

Raw Parallel Corpora
        │
        ▼
Text Normalization
        │
        ▼
Structural / Consistency Filtering
        │
        ▼
Deduplication
        │
        ▼
Language Identification
        │
        ▼
Semantic Alignment Filtering
        │
        ▼
Filtered English–Vietnamese Corpus

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Basic Data Filtering

Basic filtering was applied to remove malformed, noisy, or obviously misaligned sentence pairs before more expensive model-based filtering.

Alphabetic Ratio Filtering

Both source and target sentences were required to contain sufficient alphabetic content.

The minimum alphabetic-character ratio was:

0.7

Sentence pairs in which either side fell below this threshold were removed.

This step helps eliminate samples dominated by punctuation, markup, numbers, symbols, or corrupted text.

Special-Character De-escaping

HTML entities and escaped TSV characters were converted back to their normal textual representations.

Examples include:

&   → &
&lt;    → <
&gt;    → >
\t      → tab / normalized whitespace

Whitespace Normalization

Repeated whitespace characters were collapsed into a single space.

For example:

"This   is    a sentence."

becomes:

"This is a sentence."

Empty-Sentence Removal

Sentence pairs were discarded when either the English or Vietnamese sequence was empty after normalization.

Numerical Consistency Filtering

Sentence pairs containing inconsistent numerical information between source and target were removed.

This helps eliminate alignment errors in which important quantities, dates, measurements, percentages, or other numerical values differ between the two sentences.

Currency Consistency Filtering

Samples containing inconsistent currency information between source and target were discarded.

This filtering reduces translation pairs where financial values or currency symbols were incorrectly aligned.

URL Consistency Filtering

URLs appearing in one side of a sentence pair were checked against URLs appearing in the corresponding translation.

Pairs containing inconsistent URL information were removed.

Email Consistency Filtering

Email addresses were similarly checked between the source and target sentences.

Pairs containing mismatched email addresses were discarded.

Control-Character Removal

Non-printable and unwanted control characters were removed from the text.

Deduplication

Duplicate source–target sentence pairs were detected using hash-based deduplication and removed.

This reduces unnecessary repetition and prevents highly duplicated samples from disproportionately influencing the training distribution.

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Language Identification

After basic filtering, language identification was performed using a FastText language identification model.

The expected language configuration is:

Source: English
Target: Vietnamese

Sentence pairs were removed when the detected languages did not match the expected English–Vietnamese language pair.

This stage is particularly useful for large web-crawled corpora, where incorrectly classified languages and multilingual fragments may occur frequently.

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Semantic Alignment Filtering

Language correctness alone does not guarantee that two sentences are valid translations of each other.

To detect semantically misaligned pairs, the dataset uses LaBSE — Language-agnostic BERT Sentence Embedding.

Each English sentence and its corresponding Vietnamese sentence are encoded into a shared multilingual embedding space.

Conceptually:

English sentence ──► LaBSE ──► embedding_en
                                  │
                                  │ cosine similarity
                                  ▼
Vietnamese sentence ─► LaBSE ─► embedding_vi

The semantic similarity between both embeddings is then measured.

Only sentence pairs satisfying:

similarity >= 0.8

are retained.

This stage removes sentence pairs that may contain valid English and Vietnamese text individually but do not express sufficiently similar semantic content.

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Filtering Strategy

The filtering process follows a coarse-to-fine design:

Cheap deterministic filters
          │
          ▼
Remove obvious noise
          │
          ▼
Language identification
          │
          ▼
Semantic embedding model
          │
          ▼
High-quality parallel pairs

Computationally inexpensive operations such as normalization, numerical checks, and deduplication are performed before FastText and LaBSE inference.

This reduces the number of samples that need to pass through the more computationally expensive neural filtering stages.

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Intended Uses

The dataset is primarily intended for:

• English → Vietnamese machine translation
• Vietnamese → English machine translation
• Neural Machine Translation pretraining
• Translation model fine-tuning
• Sequence-to-sequence modeling
• Cross-lingual representation learning
• English–Vietnamese alignment research
• Data filtering and machine translation experiments

It can be used with architectures such as:

Transformer Encoder–Decoder
        │
        ├── Transformer-base / Transformer-big
        ├── mBART
        ├── mT5
        ├── NLLB
        └── other multilingual sequence-to-sequence models

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Data Quality Considerations

The filtering pipeline significantly reduces common sources of noise, but it does not guarantee perfect translation equivalence.

Residual issues may include:

• subtle semantic mismatches;
• incomplete translations;
• domain-specific terminology errors;
• named-entity inconsistencies not captured by the filtering rules;
• unusual formatting inherited from source datasets;
• naturally noisy subtitle or web-crawled text;
• duplicate or near-duplicate sentences not detected by exact hash-based deduplication.

The LaBSE threshold of 0.8 prioritizes stronger semantic alignment but may also remove legitimate translation pairs whose lexical or structural forms differ substantially across languages.

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Dataset Provenance

This dataset is an aggregated and filtered derivative corpus constructed from multiple external datasets.

Users should consult the original datasets for information regarding:

• licensing;
• redistribution conditions;
• attribution requirements;
• source-specific limitations;
• copyright restrictions.

Because the aggregate dataset combines sources with potentially different licensing conditions, the dataset-level license is currently declared as:

license: unknown

The licenses and terms of the original data sources remain applicable to their respective data.

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Keywords

English-Vietnamese
Machine Translation
Parallel Corpus
Neural Machine Translation
NMT
NLP
OPUS
FastText
LaBSE
Semantic Filtering
Language Identification
Data Cleaning
Cross-lingual Learning