@inproceedings{yadav-etal-2022-normalization,
    title = "Normalization of Spelling Variations in Code-Mixed Data",
    author = "Yadav, Krishna  and
      Akhtar, Md  and
      Chakraborty, Tanmoy",
    editor = "Akhtar, Md. Shad  and
      Chakraborty, Tanmoy",
    booktitle = "Proceedings of the 19th International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2022",
    address = "New Delhi, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.icon-main.33/",
    pages = "269--279",
    abstract = "Code-mixed text infused with low resource language has always been a challenge for natural language understanding models. A significant problem while understanding such texts is the correlation between the syntactic and semantic arrangement of words. The phonemes of each character in a word dictates the spelling representation of a term in low resource language. However, there is no universal protocol or alphabet mapping for code-mixing. In this paper, we highlight the impact of spelling variations in code-mixed data for training natural language understanding models. We emphasize the impact of using phonetics to neutralize this variation in spelling across different usage of a word with the same semantics. The proposed approach is a computationally inexpensive technique and improves the performances of state-of-the-art models for three dialog system tasks \textit{viz.} intent classification, slot-filling, and response generation. We propose a data pipeline for normalizing spelling variations irrespective of language."
}