Biases in data
Biases in human-created content within training data can have a direct impact on NLP models, leading to biased results.
Naturally, represents specific demographics or viewpoints, NLP models trained on that data are more likely to inadvertently reinforce those same biases.
Ambiguity and polysemy
NLP models are challenged with deciphering the correct context for numerous words and phrases that have multiple meanings.
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Deciphering the intended meaning in a given context is no easy task. For example, the word "bank" can refer to a financial institution or to the bank of a river.
Multilingual understanding
The complexity of multilingual NLP lies in the challenge of maintaining the original meaning, tone and cultural subtleties when translating or generating text.
When it comes to translating content into different languages, linguistic accuracy alone is not enough. Cultural sensitivity is equally important to effectively convey the desired message.
Code switching and multilingual texts
When it comes to multilingual natural language processing (NLP), the challenge lies in accurately understanding texts that include a mix of languages or smooth transitions between them.
Code switching is common in social media messages and conversations, which requires NLP models to correctly interpret these texts in multiple languages.