Srivastava, VivekVivekSrivastavaSingh, MayankMayankSingh2025-08-312025-08-312021-01-01[9781954085886]10.26615/978-954-452-056-4_0202-s2.0-85129001417http://repository.iitgn.ac.in/handle/IITG2025/26393Text 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 inefficacy 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.trueConference Paperhttps://doi.org/10.26615/978-954-452-056-4_020200-208202112cpConference Proceeding1