To address the challenges of error propagation, overlapping triple problem and subject-object alignment in the existing relationship triplet extraction methods, this study proposes a novel bidirectional translation and decoding model. The model reframes the extraction process into three sub-tasks: entity extraction, subject-object alignment and relationship judgment. The bidirectional structure effectively alleviates error propagation, while the translation and decoding method based on the attention mechanism deals with the overlapping triple problem and aligns the subject and object. Finally, a bipartite entity-torelationship diagram fully explores the relationship between entity pairs, enabling accurate relationship judgment. Experimental results on public datasets have validated the performance of the proposed model.
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