Reflection-based Word Attribute Transfer
원문 초록
Word embeddings, which often represent such analogic relations as king - man + woman = queen, can be used to change a word's attribute, including its gender. For transferring king into queen in this analogy-based manner, we subtract a difference vector man - woman based on the knowledge that king is male. However, developing such knowledge is very costly for words and attributes. In this work, we propose a novel method for word attribute transfer based on reflection mappings without such an analogy operation. Experimental results show that our proposed method can transfer the word attributes of the given words without changing the words that do not have the target attributes.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
On the Emergence of Linear Analogies in Word Embeddings
Entity Resolution with Hierarchical Graph Attention Networks
Attributes Shape the Embedding Space of Face Recognition Models
Ref-Long: Benchmarking the Long-context Referencing Capability of Long-context Language Models
Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations
MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection
토론
이 논문과 연결된 포럼 글이 아직 없습니다.