Semantics and Pragmatic Understanding: It is a huge challenge to understand the semantic and pragmatic meaning of a language. Many times, the meaning of words and their pragmatic messages may differ, making it difficult for models to understand what a phrase means.
Language Diversity: The diversity of styles, dramatics and punctuation in language is another challenge. Proper understanding of the same word or sentence from different contexts can be problematic.
Challenge of Translation: Translating languages is yet another uphill task due to the need for linguisticism, semantic descriptions and coherence in language.
Availability of Data: Also, lack of big data with high quality is a challenge since practicing with good and balanced dataset can help improve models.
Deep Learning: Large volume of data is necessary for more profound education so that models would succeed even in fine-grained analysis of language.
Social pragmatics: Natural Language Processing models find it hard to comprehend social pragmatics, which make them incapable to correctly empathize with users’ feelings.