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- W3202284475 abstract "Transformers changed modern NLP in many ways. However, they can hardly exploit domain knowledge, and like other blackbox models, they lack interpretability. Unfortunately, structured knowledge injection, in the long run, risks to suffer from a knowledge acquisition bottleneck. We thus propose a memory enhancement of transformer models that makes use of unstructured domain knowledge expressed in plain natural language. An experimental evaluation conducted on two challenging NLP tasks demonstrates that our approach yields better performance and model interpretability than baseline transformer-based architectures." @default.
- W3202284475 created "2021-10-11" @default.
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- W3202284475 date "2021-09-02" @default.
- W3202284475 modified "2023-09-23" @default.
- W3202284475 title "MemBERT: Injecting Unstructured Knowledge into BERT" @default.
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