language:ind machine-translation region:us
"In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language,
and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic.
In this paper, we show otherwise by collecting large, publicly-available datasets from the Web, which we split into several domains: news, religion, general, and
conversation,to train and benchmark some variants of transformer-based NMT models across the domains.
We show using BLEU that our models perform well across them , outperform the baseline Statistical Machine Translation (SMT) models,
and perform comparably with Google Translate. Our datasets (with the standard split for training, validation, and testing), code, and models are available on https://github.com/gunnxx/indonesian-mt-data."
@inproceedings{guntara-etal-2020-benchmarking,
    title = "Benchmarking Multidomain {E}nglish-{I}ndonesian Machine Translation",
    author = "Guntara, Tri Wahyu  and
      Aji, Alham Fikri  and
      Prasojo, Radityo Eko",
    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2020.bucc-1.6",
    pages = "35--43",
    language = "English",
    ISBN = "979-10-95546-42-9",
}
---
tags:
- machine-translation
language:
- ind
---

# indo_general_mt_en_id

"In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language,

and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic.

In this paper, we show otherwise by collecting large, publicly-available datasets from the Web, which we split into several domains: news, religion, general, and

conversation,to train and benchmark some variants of transformer-based NMT models across the domains.

We show using BLEU that our models perform well across them , outperform the baseline Statistical Machine Translation (SMT) models,

and perform comparably with Google Translate. Our datasets (with the standard split for training, validation, and testing), code, and models are available on https://github.com/gunnxx/indonesian-mt-data."

## Dataset Usage

Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.

## Citation

```
@inproceedings{guntara-etal-2020-benchmarking,
    title = "Benchmarking Multidomain {E}nglish-{I}ndonesian Machine Translation",
    author = "Guntara, Tri Wahyu  and
      Aji, Alham Fikri  and
      Prasojo, Radityo Eko",
    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2020.bucc-1.6",
    pages = "35--43",
    language = "English",
    ISBN = "979-10-95546-42-9",
}
```

## License

Creative Commons Attribution Share-Alike 4.0 International

## Homepage

[https://github.com/gunnxx/indonesian-mt-data](https://github.com/gunnxx/indonesian-mt-data)

### NusaCatalogue

For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)