@inproceedings{srivastava-singh-2021-hinge,
    title = "{H}in{GE}: A Dataset for Generation and Evaluation of Code-Mixed {H}inglish Text",
    author = "Srivastava, Vivek  and
      Singh, Mayank",
    editor = "Gao, Yang  and
      Eger, Steffen  and
      Zhao, Wei  and
      Lertvittayakumjorn, Piyawat  and
      Fomicheva, Marina",
    booktitle = "Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.eval4nlp-1.20/",
    doi = "10.18653/v1/2021.eval4nlp-1.20",
    pages = "200--208",
    abstract = "Text generation is a highly active area of research in the computational linguistic community. The evaluation of the generated text is a challenging task and multiple theories and metrics have been proposed over the years. Unfortunately, text generation and evaluation are relatively understudied due to the scarcity of high-quality resources in code-mixed languages where the words and phrases from multiple languages are mixed in a single utterance of text and speech. To address this challenge, we present a corpus (HinGE) for a widely popular code-mixed language Hinglish (code-mixing of Hindi and English languages). HinGE has Hinglish sentences generated by humans as well as two rule-based algorithms corresponding to the parallel Hindi-English sentences. In addition, we demonstrate the in- efficacy of widely-used evaluation metrics on the code-mixed data. The HinGE dataset will facilitate the progress of natural language generation research in code-mixed languages."
}