Papers with contextual embedding model
Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations (2024.naacl-long)
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Sihao Chen, Hongming Zhang, Tong Chen, Ben Zhou, Wenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang, Dan Roth, Dong Yu
| Challenge: | Sentence embeddings are typically learned to recognize the semantic relation between two text inputs. |
| Approach: | They introduce a contrastively-learned contextual embedding model for fine-grained semantic representation of text. |
| Outcome: | The proposed model is able to produce contextual embeddings corresponding to different atomic propositions, i.e. semantic equivalence between propositions across different text sequences. |
UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus (2021.naacl-main)
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| Challenge: | Contextual word embedding models do not take into account structured expert domain knowledge from a knowledge base. |
| Approach: | They propose a contextual embedding model that integrates domain knowledge during the pre-training process via a novel knowledge augmentation strategy. |
| Outcome: | The proposed model outperforms existing domain-specific models on common named-entity recognition (NER) and clinical natural language inference tasks. |