---
configs:
- config_name: default
  data_files:
  - split: train
    path: "train.csv"
  - split: dev
    path: "dev.csv"
  - split: test
    path: "test.csv"
---

# STIF-Indonesia

![Paper](imgs/meme_stif.PNG)

A dataset of ["Semi-Supervised Low-Resource Style Transfer of Indonesian Informal to Formal Language with Iterative Forward-Translation"](https://arxiv.org/abs/2011.03286v1).

You can also find Indonesian informal-formal parallel corpus in this repository.

## Description

We were researching transforming a sentence from informal to its formal form. Our work addresses a style-transfer from informal to formal  Indonesian as a low-resource **machine translation**  problem. We benchmark several strategies to perform the style transfer.

In this repository, we provide the Phrase-Based Statistical Machine Translation, which has the highest result in our experiment. Note that, our data is extremely low-resource and domain-specific (Customer Service domain). Therefore, the system might not be robust towards out-of-domain input. Our future work includes exploring more robust style transfer. Stay tuned!

## Paper

![Paper](imgs/paper.PNG)

You can access our paper below:

[Semi-Supervised Low-Resource Style Transfer of Indonesian Informal to Formal Language with Iterative Forward-Translation (IALP 2020)](https://arxiv.org/abs/2011.03286v1)

## Team

1. Haryo Akbarianto Wibowo @ Kata.ai
2. Tatag Aziz Prawiro @ Universitas Indonesia
3. Muhammad Ihsan @ Bina Nusantara
4. Alham Fikri Aji @ Kata.ai
5. Radityo Eko Prasojo @ Kata.ai
6. Rahmad Mahendra @ Universitas Indonesia