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        <title>Enhancing Question Answering by Injecting Ontological Knowledge through Regularization</title>
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        <namePart type="given">Travis</namePart>
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            <title>Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures</title>
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        <name type="personal">
            <namePart type="given">Eneko</namePart>
            <namePart type="family">Agirre</namePart>
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        <name type="personal">
            <namePart type="given">Marianna</namePart>
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        <name type="personal">
            <namePart type="given">Ivan</namePart>
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    <abstract>Deep neural networks have demonstrated high performance on many natural language processing (NLP) tasks that can be answered directly from text, and have struggled to solve NLP tasks requiring external (e.g., world) knowledge. In this paper, we present OSCR (Ontology-based Semantic Composition Regularization), a method for injecting task-agnostic knowledge from an Ontology or knowledge graph into a neural network during pre-training. We evaluated the performance of BERT pre-trained on Wikipedia with and without OSCR by measuring the performance when fine-tuning on two question answering tasks involving world knowledge and causal reasoning and one requiring domain (healthcare) knowledge and obtained 33.3%, 18.6%, and 4% improved accuracy compared to pre-training BERT without OSCR.</abstract>
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    <identifier type="doi">10.18653/v1/2020.deelio-1.7</identifier>
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%0 Conference Proceedings
%T Enhancing Question Answering by Injecting Ontological Knowledge through Regularization
%A Goodwin, Travis
%A Demner-Fushman, Dina
%Y Agirre, Eneko
%Y Apidianaki, Marianna
%Y Vulić, Ivan
%S Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F goodwin-demner-fushman-2020-enhancing
%X Deep neural networks have demonstrated high performance on many natural language processing (NLP) tasks that can be answered directly from text, and have struggled to solve NLP tasks requiring external (e.g., world) knowledge. In this paper, we present OSCR (Ontology-based Semantic Composition Regularization), a method for injecting task-agnostic knowledge from an Ontology or knowledge graph into a neural network during pre-training. We evaluated the performance of BERT pre-trained on Wikipedia with and without OSCR by measuring the performance when fine-tuning on two question answering tasks involving world knowledge and causal reasoning and one requiring domain (healthcare) knowledge and obtained 33.3%, 18.6%, and 4% improved accuracy compared to pre-training BERT without OSCR.
%R 10.18653/v1/2020.deelio-1.7
%U https://aclanthology.org/2020.deelio-1.7
%U https://doi.org/10.18653/v1/2020.deelio-1.7
%P 56-63

Markdown (Informal)

[Enhancing Question Answering by Injecting Ontological Knowledge through Regularization](https://aclanthology.org/2020.deelio-1.7) (Goodwin & Demner-Fushman, DeeLIO 2020)

• Enhancing Question Answering by Injecting Ontological Knowledge through Regularization (Goodwin & Demner-Fushman, DeeLIO 2020)

ACL

• Travis Goodwin and Dina Demner-Fushman. 2020. Enhancing Question Answering by Injecting Ontological Knowledge through Regularization. In Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 56–63, Online. Association for Computational Linguistics.