Natural language processing (NLP) is a growing field within artificial intelligence. The
fundamental goal of NLP is to program computers capable of human-level understanding of
natural language. Common NLP applications include personal assistants and chatbots,
automatic translation, question answering, sentiment analysis and summarization. Among
the main challenges of NLP research is that human language is often ambiguous and
underspecified. A person processing language relies heavily on their commonsense
knowledge and reasoning abilities to resolve these ambiguities and complete missing
information. Machine learning based NLP models, on the other hand, lack this commonsense
and often make absurd mistakes.
In this course, we will discuss the various topics, including the following:
- What is commonsense and why do we need it in NLP? How does it relate to earlier
attempts in AI to teach machines commonsense?
- How do we measure commonsense reasoning abilities? How good are our existing
models?
- How can commonsense be acquired? What are the many challenges in acquiring
commonsense knowledge?
- How can we incorporate such knowledge into NLP models?
- Can we teach computers to reason?
The course assumes you have an understanding of machine learning and deep learning, and
basic familiarity with NLP. If you haven't taken a previous NLP course, I will refer you to
online tutorials. The course will involve attending class, reading, presenting, and
discussing papers, two homework assignments, and a research project on an idea you are
passionate
about in this space.