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README.md

@@ -3,4 +3,45 @@ tags:
 - machine-translation
 language:
 - ind
----
+---
+
+"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
+
+### NusaCatalogue
+
+For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)