2024
EACL
EACL 2024
Machine-in-the-Loop with Documentary and Descriptive Linguists
Abstract
AbstractThis paper describes a curriculum for teaching linguists how to apply machine-in-the-loop (MitL) approach to documentary and descriptive tasks. It also shares observations about the learning participants, who are primarily non-computational linguists, and how they interact with the MitL approach. We found that they prefer cleaning over increasing the training data and then proceed to reanalyze their analytical decisions, before finally undertaking small actions that emphasize analytical strategies. Overall, participants display an understanding of the curriculum which covers fundamental concepts of machine learning and statistical modeling.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Machine Learning
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio