1. Natural Language Understanding
Broadly: any computational problem where the input is natural language, and the output is structured information that a computer can store (e.g. in a database) or execute (e.g. a command to a digital assistant).
1.1. Digital Assistants
1.2. Question answering
1.3. Sentiment analysis
1.4. Syntactic parsing
1.5. Semantic parsing
2. Natural Language Generation
Broadly: any computational problem where the input is non-linguistic data (e.g. data, images, sound) and the output is a natural language description of the input.
2.1. Data-to-text generation
2.2. Image caption
3. Machine Translation
Both input and output are text that convey the same meaning, but written in a different language or style.
Philosophically and technically, machine translation requires both NLU and NLG.
3.1. Language Translation
3.2. Text simplification
3.3. Summarization
4. About Deep Learning in NLP
4.1. Idea: Deep learning simplifies machine learning
Why has deep learning taken over NLP?
- Deep learning simplifies the design of probabilistic models, by replacing complex dependencies and independence assumptions with universal function approximators.
- Deep learning gives us representation learning: data representations are learned rather than engineered.
- Learned representations are easy to obtain and reusable, enabling multi-task learning.
- Deep learning provides a uniform, flexible, trainable framework that can easily mix and match different data types: strings, labels, trees, graphs, data, and images.
In short: deep learning solves the difficulties of applying machine learning to NLP… But it does not solve NLP!
4.2. Problem: Deep learning technology is energy intensive
4.3. Problem: Ethical practice lags technical practice
Modern NLP originated in laboratory experiments with machine learning methods on linguistically annotated text. But NLP has escaped the lab, and can have a direct effect on people’s lives
There are many wider ethical concerns about ML/data science, e.g., privacy. We’ll focus on NLP in the course, but specific problems in NLP often reflect more general problems.
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