@inproceedings{singh-etal-2020-urdu,
    title = "{U}rdu To {P}unjabi Machine Translation System",
    author = "Singh, Umrinder Pal  and
      Goyal, Vishal  and
      Lehal, Gurpreet",
    editor = "Goyal, Vishal  and
      Ekbal, Asif",
    booktitle = "Proceedings of the 17th International Conference on Natural Language Processing (ICON): System Demonstrations",
    month = dec,
    year = "2020",
    address = "Patna, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.icon-demos.6/",
    pages = "16--18",
    abstract = "Machine Translation is a popular area of NLP research field. There are various approaches to develop a machine translation system like Rule-Based, Statistical, Neural and Hybrid. A rule-Based system is based on grammatical rules and uses bilingual lexicons. Statistical and Neural use the large parallel corpus for training the respective models. Where the Hybrid MT system is a mixture of different approaches. In these days the corpus-based machine translation system is quite popular in NLP research area. But these models demands huge parallel corpus. In this research, we have used a hybrid approach to develop Urdu to Punjabi machine translation system. In the developed system, statistical and various sub-system based on the linguistic rule has been used. The system yield 80{\%} accuracy on a different set of the sentence related to domains like Political, Entertainment, Tourism, Sports and Health. The complete system has been developed in a C{\#}.NET programming language."
}