Microsoft Azure AI Fundamentals (AI-900)Describe features of Natural Language Processing (NLP) workloads on AzureHard
A global tech company wants to ensure that all internal communications, regardless of the original language, adhere to company policy and do not contain sensitive or inappropriate content. Which combination of NLP capabilities would be most effective for this task?
- ANamed Entity Recognition and Topic Modeling
- BLanguage Detection and Text Generation
- CSpeech-to-Text and Form Recognizer
- DMachine Translation and Content Moderation
Show answer & explanationAnswer & explanation
Correct answer: D. Machine Translation and Content Moderation
To check communications in 'any language', Machine Translation is needed first to convert all text into a common language (e.g., English). Then, Content Moderation can scan the translated text for policy violations, sensitive or inappropriate content. This combination directly addresses the problem of multilingual policy adherence.
Why the other options are wrong
- A. NER identifies entities, and Topic Modeling identifies themes, but neither directly moderates content across languages.
- B. Text Generation creates new text, not for checking existing content across languages.
- C. Speech-to-Text converts audio, and Form Recognizer extracts data from structured documents, neither directly addresses multilingual content moderation.
Multilingual Content Moderation
The process of identifying and filtering inappropriate or policy-violating content across multiple languages.
- Often requires Machine Translation as a precursor.
- Utilizes NLP for sentiment, toxicity, and policy checks.
- Essential for global communication platforms.
Memory trick: Translate to understand, then moderate to comply.