Cue prompt adapting model for relation extraction
Prompt-tuning models output relation types as verbalised-type tokens instead of predicting the confidence scores for each relation type. However, existing prompt-tuning models cannot perceive named entities of a relation instance because they are normally implemented on raw input that is too weak to...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2161478 |
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| Summary: | Prompt-tuning models output relation types as verbalised-type tokens instead of predicting the confidence scores for each relation type. However, existing prompt-tuning models cannot perceive named entities of a relation instance because they are normally implemented on raw input that is too weak to encode the contextual features and semantic dependencies of a relation instance. This study proposes a cue prompt adapting (CPA) model for relation extraction (RE) that encodes contextual features and semantic dependencies by implanting task-relevant cues in a sentence. Additionally, a new transformer architecture is proposed to adapt pre-trained language models (PLMs) to perceive named entities in a relation instance. Finally, in the decoding process, a goal-oriented prompt template is designed to take advantage of the potential semantic features of a PLM. The proposed model is evaluated using three public corpora: ACE, ReTACRED, and Semeval. The performance achieves an impressive improvement, outperforming existing state-of-the-art models. Experiments indicate that the proposed model is effective for learning task-specific contextual features and semantic dependencies in a relation instance. |
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| ISSN: | 0954-0091 1360-0494 |