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.- Anthology ID:
- D19-6010
- Volume:
- Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Simon Ostermann, Sheng Zhang, Michael Roth, Peter Clark
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 85–92
- Language:
- URL:
- https://aclanthology.org/D19-6010
- DOI:
- 10.18653/v1/D19-6010
- Cite (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.
- Cite (Informal):
- Jeff Da at COIN - Shared Task: BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge (Da, 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D19-6010.pdf