Abstract
HESIP is a hybrid machine learning system in which a sub-symbolic machine learning component makes a prediction for an image classification and afterwards a symbolic machine learning component learns probabilistic rules that are used to explain that prediction. In this paper, we present an extension to HESIP that generates human-understandable and machine-processable explanations in a controlled natural language for the learned probabilistic rules. In order to achieve this, the literals of the probabilistic rules are first reordered, and then aggregated and disambiguated according to linguistic principles so that the rules can be verbalised with a bi-directional grammar. A human-in-the-loop can modify incorrect explanations and the same bi-directional grammar can be used to process these explanations to improve the decision process of the machine.
Original language | English |
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Title of host publication | Seventh International Workshop on Controlled Natural Language, CNL 2020/21 |
Subtitle of host publication | Workshop Proceedings |
Editors | Tobias Kuhn, Silvie Spreeuwenberg, Stijn Hoppenbrouwers, Norbert E. Fuchs |
Place of Publication | Stroudsburg, PA |
Publisher | Association for Computational Linguistics (ACL) |
Number of pages | 5 |
Publication status | Published - 2021 |
Event | 7th International Workshop on Controlled Natural Language, CNL 2020/21 - Amsterdam, Netherlands Duration: 8 Sept 2021 → 9 Sept 2021 |
Conference
Conference | 7th International Workshop on Controlled Natural Language, CNL 2020/21 |
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Country/Territory | Netherlands |
City | Amsterdam |
Period | 8/09/21 → 9/09/21 |