metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: train.csv
      - split: dev
        path: dev.csv
      - split: test
        path: test.csv
dataset_info:
  features:
    - name: informal
      dtype: string
    - name: formal
      dtype: string
  splits:
    - name: train
      num_bytes: 344179
      num_examples: 1922
    - name: dev
      num_bytes: 37065
      num_examples: 214
    - name: test
      num_bytes: 66682
      num_examples: 363
  download_size: 276834
  dataset_size: 447926
license: mit
task_categories:
  - translation
  - text2text-generation
language:
  - id
size_categories:
  - 1K<n<10K

Dataset Card for "stif-indonesia"

STIF-Indonesia

A dataset of "Semi-Supervised Low-Resource Style Transfer of Indonesian Informal to Formal Language with Iterative Forward-Translation".

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

You can access our paper below:

Semi-Supervised Low-Resource Style Transfer of Indonesian Informal to Formal Language with Iterative Forward-Translation (IALP 2020)

Team

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