Kformer: Knowledge Injection in Transformer Feed-Forward Layers
- URL: http://arxiv.org/abs/2201.05742v1
- Date: Sat, 15 Jan 2022 03:00:27 GMT
- Title: Kformer: Knowledge Injection in Transformer Feed-Forward Layers
- Authors: Yunzhi Yao, Shaohan Huang, Ningyu Zhang, Li Dong, Furu Wei, Huajun
Chen
- Abstract summary: We propose a novel knowledge fusion model, namely Kformer, which incorporates external knowledge through the feed-forward layer in Transformer.
We empirically find that simply injecting knowledge into FFN can facilitate the pre-trained language model's ability and facilitate current knowledge fusion methods.
- Score: 107.71576133833148
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Knowledge-Enhanced Model have developed a diverse set of techniques for
knowledge integration on different knowledge sources. However, most previous
work neglect the language model's own ability and simply concatenate external
knowledge at the input. Recent work proposed that Feed Forward Network (FFN) in
pre-trained language model can be seen as an memory that stored factual
knowledge. In this work, we explore the FFN in Transformer and propose a novel
knowledge fusion model, namely Kformer, which incorporates external knowledge
through the feed-forward layer in Transformer. We empirically find that simply
injecting knowledge into FFN can enhance the pre-trained language model's
ability and facilitate current knowledge fusion methods. Our results on two
benchmarks in the commonsense reasoning (i.e., SocialIQA) and medical question
answering (i.e., MedQA-USMLE) domains demonstrate that Kformer can utilize
external knowledge deeply and achieves absolute improvements in these tasks.
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