2022/04/19 by Mareike Hartmann, Hartmann, Mareike, Daniel Sonntag +1 · 4 citations
Computer Science · #Computation and Language (cs.CL) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Semantic Web and Ontologies #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.2204.08892
To be published in the Proceedings of the The First Workshop on Learning with Natural Language Supervision
arxiv created 2022/04/19 · openalex publication_date 2022/04/19 · arxiv updated 2022/04/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Training a model with access to human explanations can improve data efficiency and model performance on in- and out-of-domain data. Adding to these empirical findings, similarity with the process of human learning makes learning from explanations a promising way to establish a fruitful human-machine interaction. Several methods have been proposed for improving natural language processing (NLP) models with human explanations, that rely on different explanation types and mechanism for integrating these explanations into the learning process. These methods are rarely compared with each other, making it hard for practitioners to choose the best combination of explanation type and integration mechanism for a specific use-case. In this paper, we give an overview of different methods for learning from human explanations, and discuss different factors that can inform the decision of which method to choose for a specific use-case.