Empowering Active Learning to Jointly Optimize System and User Demands

Ji-Ung Lee, Christian M. Meyer, Iryna Gurevych


Abstract
Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training. However, when active learning is integrated with an end-user application, this can lead to frustration for participating users, as they spend time labeling instances that they would not otherwise be interested in reading. In this paper, we propose a new active learning approach that jointly optimizes the seemingly counteracting objectives of the active learning system (training efficiently) and the user (receiving useful instances). We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user, while the users should receive only exercises that match their skills. We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.
Anthology ID:
2020.acl-main.390
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4233–4247
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.390
DOI:
10.18653/v1/2020.acl-main.390
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.390.pdf
Software:
 2020.acl-main.390.Software.zip
Video:
 http://slideslive.com/38928998