Complexity of human language , including cultural nuances and contextual understanding , presents ongoing challenges for NLP systems .
“ Contextual understanding is critical based on cultural norms , and we have to build models that are situationally and geographically aware if we are to ensure a more natural engagement between digital and human over time ,” explains Brad .
This cultural and linguistic diversity adds another layer of complexity to NLP development and deployment .
Sebastian notes : “ Excluding non -English speakers , or imbuing NLP systems with only the values and cultural context of a small development team , effectively excludes the majority of the global population .”
This lack of understanding may preclude those of different cultures , and therefore different ways of operating , from getting the best out of these NLP systems .
The data dilemma : quality over quantity These issues mentioned above are focused on the application side , but really , their root stems from the same place : the data .
Accuracy and effectiveness of NLP systems heavily depend on the quality and diversity of the data they ’ re trained on . But ensuring this data is representative , unbiased , and of high quality is a laborious and complex task .
For large tech companies with vast resources , this may not be as significant an issue . However , in the age of AI
110 September 2024