|Time: Term 2 (Jan-Apr 2022), MW 9:00-10:30||Location: DMP 101|
|Instructor: Vered Shwartz||Office hours: TBD|
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?