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            <title>Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing</title>
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    <abstract>We introduce a simple yet effective method of integrating contextual embeddings with commonsense graph embeddings, dubbed BERT Infused Graphs: Matching Over Other embeDdings. First, we introduce a preprocessing method to improve the speed of querying knowledge bases. Then, we develop a method of creating knowledge embeddings from each knowledge base. We introduce a method of aligning tokens between two misaligned tokenization methods. Finally, we contribute a method of contextualizing BERT after combining with knowledge base embeddings. We also show BERTs tendency to correct lower accuracy question types. Our model achieves a higher accuracy than BERT, and we score fifth on the official leaderboard of the shared task and score the highest without any additional language model pretraining.</abstract>
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%0 Conference Proceedings
%T Jeff Da at COIN - Shared Task: BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge
%A Da, Jeff
%Y Ostermann, Simon
%Y Zhang, Sheng
%Y Roth, Michael
%Y Clark, Peter
%S Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F da-2019-jeff
%X We introduce a simple yet effective method of integrating contextual embeddings with commonsense graph embeddings, dubbed BERT Infused Graphs: Matching Over Other embeDdings. First, we introduce a preprocessing method to improve the speed of querying knowledge bases. Then, we develop a method of creating knowledge embeddings from each knowledge base. We introduce a method of aligning tokens between two misaligned tokenization methods. Finally, we contribute a method of contextualizing BERT after combining with knowledge base embeddings. We also show BERTs tendency to correct lower accuracy question types. Our model achieves a higher accuracy than BERT, and we score fifth on the official leaderboard of the shared task and score the highest without any additional language model pretraining.
%R 10.18653/v1/D19-6010
%U https://aclanthology.org/D19-6010
%U https://doi.org/10.18653/v1/D19-6010
%P 85-92

Markdown (Informal)

[Jeff Da at COIN - Shared Task: BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge](https://aclanthology.org/D19-6010) (Da, 2019)

• Jeff Da at COIN - Shared Task: BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge (Da, 2019)

ACL

• Jeff Da. 2019. Jeff Da at COIN - Shared Task: BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge. In Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing, pages 85–92, Hong Kong, China. Association for Computational Linguistics.