Low-Resource

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 ...

NLP in Global Languages: Challenges and Solutions Global language diversity presents both a promise and a hurdle for natural language processing. While NLP has become reliable for major languages, thousands of tongues stay underrepresented in data and tools. This gap affects search, translation, voice assistants, and social media moderation, especially for communities with unique scripts and rich morphology. Across languages, several challenges slow progress: Data scarcity: fewer labeled samples and smaller corpora. Script and morphology: non Latin scripts, complex word forms, diacritics. Dialects and code-switching: speakers mix languages in one sentence. Evaluation gaps: few standard benchmarks across languages. Bias and fairness: models tend to reflect dominant languages. Resources: limited compute, privacy concerns, licensing limits. Researchers and developers use several practical approaches to move forward: ...