FormosanBankDemos/formosan-mt

FormosanBank Machine Translation

Parallel corpora for 15 Indigenous Formosan languages aligned to English and Mandarin Chinese, prepared for use with the Hugging Face datasets library.

The dataset aggregates processed sentence- and phrase-level corpora into two CSV files:

• Formosan → English (formosan_en_hf.csv)
• Formosan → Chinese (formosan_zh_hf.csv)

Each row is a single bilingual sentence pair with language, dialect, split, and provenance metadata. The dataset is designed for training and evaluating neural machine translation (NMT) and related models for low-resource Formosan languages.

IMPORTANT DISCLAIMER: Our Machine Translation models published on HuggingFace and in our papers were trained on this data in addition to private data not available to the public due to content restrictions.

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Dataset Summary

• Total sentence pairs: 393,634
  • Formosan → English: 85,144
  • Formosan → Chinese: 308,490
• Languages (15): Amis, Bunun, Kavalan, Rukai, Paiwan, Puyuma, Thao, Saaroa, Sakizaya, Yami/Tao, Atayal, Seediq/Truku, Tsou, Kanakanavu, Saisiyat
• Targets: English (en), Mandarin Chinese (zh)
• Splits (all languages, both targets combined):
  • Train: 334,772
  • Validate: 29,412
  • Test: 29,450
• License: CC BY 4.0
• Format: UTF-8 CSV, one sentence pair per row

The dataset is intended to support research on low-resource MT, cross-lingual transfer, and documentation of endangered Formosan languages.

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Supported Tasks and Use Cases

Primary task

• translation
  • Formosan language → English
  • Formosan language → Chinese

Example use cases

• Training NMT systems (e.g. NLLB / encoder–decoder models) for individual Formosan languages.
• Cross-lingual pretraining and evaluation for multilingual models.
• Dialect-aware MT experiments using the dialect field.
• Lexicon / dictionary-style MT from short phrases and headwords.

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Languages and Coverage

High-level sentence counts per language (summing both directions: Formosan→English and Formosan→Chinese):

Language	Formosan→English	Formosan→Chinese	Total
Amis	10,523	30,646	41,169
Bunun	9,006	30,878	39,884
Kavalan	2,098	14,682	16,780
Rukai	11,850	39,360	51,210
Paiwan	9,806	24,015	33,821
Puyuma	7,199	26,154	33,353
Thao	2,086	11,633	13,719
Saaroa	2,130	9,819	11,949
Sakizaya	2,132	11,318	13,450
Yami/Tao	3,009	12,792	15,801
Atayal	11,724	35,471	47,195
Seediq/Truku	7,244	29,840	37,084
Tsou	2,117	8,861	10,978
Kanakanavu	2,105	11,904	14,009
Saisiyat	2,115	11,117	13,232
TOTAL	85,144	308,490	393,634

Many languages also include dialect labels, for example:

• Amis: UNKNOWN, Southern, Malan, Coastal, Xiuguluan, Hengchun
• Bunun: UNKNOWN, Junqun, Luanqun, Kaqun, Tanqun, Zhuoqun
• Paiwan, Puyuma, Rukai, Atayal, Seediq/Truku: multiple dialects
• Others (e.g. Kavalan, Thao, Saaroa, Tsou, Kanakanavu, Saisiyat, Sakizaya, Yami/Tao) currently use UNKNOWN dialect

Dialect coverage makes it possible to do dialect-specific MT or robustness studies.

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Dataset Structure

Data Files

• formosan_en_hf.csv – all Formosan→English pairs
• formosan_zh_hf.csv – all Formosan→Chinese pairs

Each file contains all languages and splits. The language direction and split are specified per row.

Data Fields

All CSVs share the same schema:

id,source_lang,target_lang,source_sentence,target_sentence,lang_code,dialect,source,split

• id (int) – unique row identifier within each file.
• source_lang (str) – language code of the Formosan language (e.g. "ami", "bnn").
• target_lang (str) – target language code ("en" or "zh").
• source_sentence (str) – sentence or phrase in the Formosan language.
• target_sentence (str) – translation into the target language.
• lang_code (str) – canonical code for the Formosan language (usually same as source_lang).
• dialect (str) – dialect label (e.g. "Southern", "Malan", "UNKNOWN").
• source (str) – provenance string or original file path in the upstream corpora.
• split (str) – one of "train", "validate", "test".

Splits

Splits are defined per row via the split column:

• train – training data
• validate – development / validate data
• test – held-out test data

Global totals across all languages and directions:

• Train: 334,772
• Validate: 29,412
• Test: 29,450

Users can filter to any language pair and then re-group into a DatasetDict by split.

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How to Load the Dataset

1. Install dependencies

pip install datasets
# optional, if you plan to fine-tune models:
pip install transformers

2. Load the EN and ZH files from the Hub

Assume the dataset identifier is:

FormosanBankDemos/formosan-mt

Load both CSVs:

from datasets import load_dataset

HF_ID = "FormosanBankDemos/formosan-mt"

# Formosan → English
ds_en_all = load_dataset(
    HF_ID,
    data_files="formosan_en_hf.csv",
)["train"]  # entire CSV exposed as a 'train' split by default

# Formosan → Chinese
ds_zh_all = load_dataset(
    HF_ID,
    data_files="formosan_zh_hf.csv",
)["train"]

Alternatively, if you rely on the YAML configs defined above:

# Uses config_name: "formosan-en" from the README metadata
ds_en_all = load_dataset(
    HF_ID,
    name="formosan-en",
    split="train",
)

3. Filter to a specific language pair (example: Amis → English, ami → en)

ami_en = ds_en_all.filter(
    lambda ex: ex["source_lang"] == "ami" and ex["target_lang"] == "en"
)

print(ami_en)
# Dataset({
#   features: ['id', 'source_lang', 'target_lang', 'source_sentence', ...],
#   num_rows: ...
# })

4. Get train / validation / test splits

from datasets import DatasetDict

def split_by_column(ds):
    return DatasetDict({
        "train":      ds.filter(lambda ex: ex["split"] == "train"),
        "validate": ds.filter(lambda ex: ex["split"] == "validate"),
        "test":       ds.filter(lambda ex: ex["split"] == "test"),
    })

ami_en_splits = split_by_column(ami_en)

print(ami_en_splits)
# DatasetDict({
#   train: Dataset({ ... })
#   validate: Dataset({ ... })
#   test: Dataset({ ... })
# })

5. (Optional) Add a translation column

Many translation training scripts expect a translation field like {"ami": "...", "en": "..."}. You can construct it from existing columns:

def add_translation(batch):
    translations = []
    for src, tgt, sl, tl in zip(
        batch["source_sentence"],
        batch["target_sentence"],
        batch["source_lang"],
        batch["target_lang"],
    ):
        translations.append({sl: src, tl: tgt})
    return {"translation": translations}

ami_en_splits = ami_en_splits.map(add_translation, batched=True)

print(ami_en_splits["train"][0]["translation"])
# {'ami': "sa'osi", 'en': 'true'}

You can reuse the same pattern for any other language pair:

# Example: Paiwan → English
pwn_en = ds_en_all.filter(
    lambda ex: ex["source_lang"] == "pwn" and ex["target_lang"] == "en"
)
pwn_en_splits = split_by_column(pwn_en)

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Intended Uses, Limitations, and Risks

Intended Uses

• Research on low-resource machine translation for Formosan languages.
• Studies of dialect variation in MT via the dialect field.
• Baseline and benchmark datasets for multilingual models focusing on Austronesian languages.

Limitations

• Domain coverage is heterogeneous (dictionary-style entries, short phrases, and some longer sentences); performance may not generalize to all real-world text genres.
• Dialect labels are not always available; some corpora use UNKNOWN for dialect.
• The dataset currently encodes translations only into English and Chinese, not between Formosan languages.

Risks and Biases

• Source corpora may contain historical, religious, or culturally specific content that is not representative of contemporary language use.
• Translations may include inconsistencies or legacy orthography; users should verify quality before high-stakes use.
• As with any MT dataset for endangered languages, there is a risk of misinterpretation or over-reliance on automatically produced translations in sensitive cultural contexts.

Users should avoid deploying models trained on this dataset in critical or high-stakes settings without human expert review.

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Citation

If you use this dataset in academic work, please cite the FormosanBank project and this dataset page. A generic citation format is:

FormosanBank annotations and metadata are CC-BY-4.0. This means you must cite the source in any redistributed or derived products. For code packages, you may refer to the GitHub repository. For academic publications, you should cite Mohamed, W., Le Ferrand, É., Sung, L.-M., Prud'hommeaux, E., & Hartshorne, J. K. (2024). FormosanBank. Electronic Resource.

FormosanBankDemos. FormosanBank Machine Translation Dataset. Hugging Face Datasets. Available at: https://huggingface.co/datasets/FormosanBankDemos/formosan-mt