Course Q&A

Computational linguistics is a fascinating field, but it comes with several core challenges:

1. Data Quality and Annotation: Ensuring high-quality, accurately annotated datasets is crucial. Poor annotation can lead to misleading results and hinder model performance.
2. Scalability: As models grow in size and complexity, they require more computational resources, making them harder to deploy in real-world applications.
3. Bias and Fairness: Large language models can inadvertently learn and propagate biases present in the training data. Addressing these biases to ensure fair and ethical AI is a significant challenge.
4. Multilingual and Cross-lingual Capabilities: Developing models that perform well across different languages and dialects remains a complex task. Cross-lingual summarization and translation are particularly challenging.
5. Pragmatics and Context Understanding: Understanding the context and pragmatics of language

The core challenges in Computational Linguistics include resolving language ambiguities, addressing data scarcity and bias, improving multilingual processing, ensuring contextual understanding, enhancing model robustness in real-world applications, and balancing ethical and privacy concerns.