metadata

license: cc-by-4.0
task_categories:
  - translation
  - text2text-generation
language:
  - en
  - hi
pretty_name: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation
size_categories:
  - 10K<n<100K

Description

PHINC is a parallel corpus for machine translation pairing code-mixed Hinglish (a fusion of Hindi and English commonly used in modern India) with human-generated English translations.

Credit

All credit goes to: PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation (Srivastava & Singh, WNUT 2020)

Original Abstract

Code-mixing is the phenomenon of using more than one language in a sentence. It is a very frequently observed pattern of communication on social media platforms. Flexibility to use mixed languages in one text message might help to communicate efficiently with the target audience. But, it adds to the challenge of processing and understanding natural language to a much larger extent. Here, we are presenting a parallel corpus of the 13,738 code-mixed English-Hindi sentences and their corresponding translation in English. The translations of sentences are done manually by the annotators. We are releasing the parallel corpus to facilitate future research opportunities for code-mixed machine translation.

Note

This data has been automatically modified to become a HuggingFace dataset (including a conversion to Parquet). The original raw dataset can be found here.