Abstract
This paper presents a way to inject and leverage existing knowledge from external sources in a Deep Learning environment, extending the recently proposed Recurrent Independent Mechnisms (RIMs) architecture, which comprises a set of interacting yet independent modules. We show that this extension of the RIMs architecture is an effective framework with lower parameter implications compared to purely fine-tuned systems.
Cite
CITATION STYLE
Bagherzadeh, P., & Bergler, S. (2021). Multi-input Recurrent Independent Mechanisms for leveraging knowledge sources: Case studies on sentiment analysis and health text mining. In Deep Learning Inside Out: 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, DeeLIO 2021 - Proceedings, co-located with the Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL-HLT 2021 (pp. 108–118). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.deelio-1.11
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