--- license: unknown task_categories: - translation language: - en - vi pretty_name: English–Vietnamese Machine Translation Dataset 2025 size_categories: - 10M \t → tab / normalized whitespace ``` ### Whitespace Normalization Repeated whitespace characters were collapsed into a single space. For example: ```text "This is a sentence." ``` becomes: ```text "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. --- ## Language Identification After basic filtering, language identification was performed using a **FastText language identification model**. The expected language configuration is: ```text 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. --- ## 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: ```text 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: ```text 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. --- ## Filtering Strategy The filtering process follows a coarse-to-fine design: ```text 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. --- ## 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: ```text Transformer Encoder–Decoder │ ├── Transformer-base / Transformer-big ├── mBART ├── mT5 ├── NLLB └── other multilingual sequence-to-sequence models ``` --- ## 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. --- ## Data Sources, Attribution, and Licensing This dataset was constructed by aggregating and filtering English–Vietnamese parallel data obtained from multiple external datasets. The inclusion of a dataset in this corpus does **not** imply that all constituent datasets share the same license or redistribution terms. Each upstream dataset remains subject to its own license, terms of use, attribution requirements, and other applicable restrictions. Users should review the original source documentation before redistributing the corpus or using it in settings that may be subject to additional legal or commercial requirements. | Source | Upstream Project / Publication | License / Redistribution Notes | Citation / Acknowledgment | |---|---|---|---| | **CCMatrix** | OPUS / CCMatrix | Consult the corpus-specific OPUS documentation and the original CCMatrix distribution terms before redistribution. | Cite OPUS and the relevant CCMatrix work where applicable. | | **OpenSubtitles** | OPUS / OpenSubtitles | Subtitle text may originate from copyrighted audiovisual works or user-contributed subtitles. Availability through OPUS should not be interpreted as transferring copyright ownership of the underlying text. Review corpus-specific terms before redistribution. | Cite OPUS and acknowledge OpenSubtitles as the upstream source. | | **MultiHPLT** | HPLT / OPUS | The OPUS MultiHPLT v2 packaging is released under **CC0**. OPUS requests acknowledgment of both the HPLT project and OPUS. | Acknowledge HPLT and OPUS. | | **CCAligned** | OPUS / CCAligned | Consult the upstream corpus documentation for source-specific licensing and redistribution conditions. | Cite OPUS and relevant CCAligned publications where applicable. | | **ParaCrawl** | ParaCrawl / OPUS | Consult the ParaCrawl release and corpus-specific OPUS documentation for the exact redistribution conditions of the version used. | Cite the relevant ParaCrawl release and OPUS where applicable. | | **PhoMT** | VinAI Research | The referenced Hugging Face copy declares **CC BY-NC-ND 4.0**. Because this license includes NonCommercial and NoDerivatives restrictions, redistribution of modified, filtered, or incorporated copies should be reviewed carefully against the terms of the original release. | Cite the original PhoMT paper. | | **MTet / VietAI MT** | VietAI | The associated MTet publication is available under CC BY 4.0; however, the license of the paper should not automatically be assumed to be the license of the dataset itself. Dataset distribution terms should be verified separately. | Cite the MTet publication and acknowledge VietAI. | --- ## Citations ### OPUS Several source corpora in this dataset were obtained through **OPUS — the Open Parallel Corpus**. When using data distributed through OPUS, please cite: ```bibtex @inproceedings{tiedemann2012opus, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, address = {Istanbul, Turkey}, publisher = {European Language Resources Association (ELRA)} } ``` Corpus-specific publications and acknowledgments should additionally be cited where required by the corresponding upstream dataset. ### PhoMT Please cite the original PhoMT publication: ```bibtex @inproceedings{doan2021phomt, title = {PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation}, author = {Doan, Long and Nguyen, Linh The and Tran, Nguyen Luong and Hoang, Thai and Nguyen, Dat Quoc}, booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing}, year = {2021}, pages = {4495--4503} } ``` ### MTet For MTet / VietAI data, please cite: ```bibtex @article{ngo2022mtet, title = {MTet: Multi-domain Translation for English and Vietnamese}, author = {Ngo, Chinh and Trinh, Trieu H. and Phan, Long and Tran, Hieu and Dang, Tai and Nguyen, Hieu and Nguyen, Minh and Luong, Minh-Thang}, journal = {arXiv preprint arXiv:2210.05610}, year = {2022} } ``` --- ## Dataset Provenance and Copyright Notice This dataset is an **aggregated and filtered derivative corpus** created from multiple independently published datasets. The dataset maintainers do not claim ownership of the original textual content contained in the upstream corpora. Copyright, database rights, neighboring rights, contractual restrictions, and other rights associated with individual source datasets or their underlying documents remain with their respective rights holders where applicable. Processing performed in this dataset includes normalization, filtering, deduplication, language identification, and semantic-alignment filtering. These operations improve corpus quality but do not supersede or replace the terms governing the original source data. ### Licensing Status Because the corpus combines data originating from sources with heterogeneous and, in some cases, potentially restrictive licensing conditions, no single permissive license is currently asserted over the entire aggregated corpus. Accordingly, the dataset metadata uses: ```yaml license: unknown ``` This designation should **not** be interpreted as meaning that the dataset is free of copyright restrictions or equivalent to public-domain material. Users are responsible for determining whether their intended use, including research use, commercial use, modification, redistribution, or publication of derived datasets, complies with the licenses and terms of the corresponding upstream sources. ### Redistribution Notice Particular care should be taken when redistributing the processed corpus. Some upstream resources may permit research use while imposing restrictions on redistribution, commercial use, or the distribution of modified versions. Where redistribution rights for a source cannot be established with sufficient confidence, a safer distribution strategy is to release: - the filtering and preprocessing code; - source identifiers and provenance metadata; - scripts for reconstructing the processed dataset from legally obtained upstream copies; - hashes or sample identifiers used for filtering; rather than redistributing the underlying sentence pairs themselves. ### No Legal Warranty The provenance and licensing information provided here is intended for documentation purposes and does not constitute legal advice. Users should consult the original license and terms of each upstream dataset when determining whether a particular form of use or redistribution is permitted. --- ## Keywords ```text English-Vietnamese Machine Translation Parallel Corpus Neural Machine Translation NMT NLP OPUS FastText LaBSE Semantic Filtering Language Identification Data Cleaning Cross-lingual Learning ```