PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation
This provides a resource for researchers working on code-mixed language processing, though it is incremental as it focuses on data creation rather than novel methods.
The paper tackles the challenge of processing code-mixed English-Hindi text on social media by creating PHINC, a parallel corpus of 13,738 sentences with manual English translations to facilitate machine translation research.
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 multiple 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. This paper presents 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 in code-mixed machine translation. The annotated corpus is available at https://doi.org/10.5281/zenodo.3605597.