Multi-input Recurrent Independent Mechanisms for leveraging knowledge sources: Case studies on sentiment analysis and health text mining

Parsa Bagherzadeh, Sabine Bergler


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.
Anthology ID:
2021.deelio-1.11
Volume:
Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
Month:
June
Year:
2021
Address:
Online
Venues:
DeeLIO | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
108–118
Language:
URL:
https://aclanthology.org/2021.deelio-1.11
DOI:
10.18653/v1/2021.deelio-1.11
Bibkey:
Cite (ACL):
Parsa Bagherzadeh and Sabine Bergler. 2021. Multi-input Recurrent Independent Mechanisms for leveraging knowledge sources: Case studies on sentiment analysis and health text mining. In Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 108–118, Online. Association for Computational Linguistics.
Cite (Informal):
Multi-input Recurrent Independent Mechanisms for leveraging knowledge sources: Case studies on sentiment analysis and health text mining (Bagherzadeh & Bergler, DeeLIO 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.deelio-1.11.pdf
Data
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