RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction
Semantic matching is a mainstream paradigm of zero-shot relation extraction, which matches a given input with a corresponding label description. The entities in the input should exactly match their hypernyms in the description, while the irrelevant contexts should be ignored when matching. However, general matching methods lack explicit modeling of the above matching pattern. In this work, we propose a fine-grained semantic matching method tailored for zero-shot relation extraction. Following the above matching pattern, we decompose the sentence-level similarity score into entity and context matching scores. Due to the lack of explicit annotations of the redundant components, we design a feature distillation module to adaptively identify the relation-irrelevant features and reduce their negative impact on context matching. Experimental results show that our method achieves higher matching $F_1$ score and has an inference speed 10 times faster, when compared with the state-of-the-art methods.
Code (1)
Tasks
RelationRelation ExtractionSentenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction
Predicting unseen relations that cannot be observed during the training phase is a challenging task in relation extraction. Previous works have made progress by matching the semantics between input instances and label de…
RelationRelation ExtractionUniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding
Vision-language tasks, such as VQA, SNLI-VE, and VCR are challenging because they require the model's reasoning ability to understand the semantics of the visual world and natural language. Supervised methods working for…
Image-text matchingSentenceText MatchingVisual Question Answering (VQA)ZeroReg: Zero-Shot Point Cloud Registration with Foundation Models
State-of-the-art 3D point cloud registration methods rely on labeled 3D datasets for training, which limits their practical applications in real-world scenarios and often hinders generalization to unseen scenes. Leveragi…
DecoderGraph MatchingObjectObject Localization+1Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification
Hierarchical text classification is an important yet challenging task due to the complex structure of the label hierarchy. Existing methods ignore the semantic relationship between text and labels, so they cannot make fu…
Classificationtext-classificationText ClassificationMulti-view Semantic Matching of Question retrieval using Fine-grained Semantic Representations
As a key task of question answering, question retrieval has attracted much attention from the communities of academia and industry. Previous solutions mainly focus on the translation model, topic model, and deep learning…
Keyword ExtractionQuestion AnsweringRetrievalSemantic Similarity+1