NLP Problems Overview — Understanding Perspective(1/2)
(You can find the Korean version of the post at this link.)
Natural Language Processing (NLP) is one of the fastest-growing areas in the field of Artificial Intelligence (AI). Recently, research has particularly focused on two types of problems. These two problem types clearly demonstrate the direction and goals of NLP research and hold significant importance from both practical and theoretical perspectives. This article is part of a series that explains each type; you can explore an overview of NLP from an ‘applied’ perspective at the following link.
The second type of problem in NLP concerns the assessment of a machine’s ‘language understanding/interpretation/inference’ capabilities, aiming to measure how well AI models comprehend language and culture. For example, this includes tasks like entailment, semantic similarity judgment, contextual meaning analysis, and multiple-choice questions. The results from such assessments can be used to evaluate the potential of the language model as a backbone model for solving various problems.
