NLP Challenges

NLP for Multilingual Applications: Challenges and Solutions Global software now often serves users in many languages. NLP helps apps understand text, answer questions, and extract meaning across languages. But multilingual work adds hurdles that single-language projects rarely face. Data gaps, diverse scripts, and different user needs require careful design and testing. Challenges in multilingual NLP Data gaps across languages and dialects Script, encoding, and tokenization differences Inconsistent terminology and domain jargon Aligning data from different languages for parallel tasks Limited benchmarks and evaluation that cover many languages Model size, latency, and deployment constraints Bias and cultural nuances in sentiment or meaning Solutions at a glance ...