Heterogeneous Dependency Graph-Guided Attentionfor Patent Representation Learning 文章

arXiv:2605.10073v2 Announce Type: replace Abstract: Pre-trained language models advance patent classification and retrieval via encoding claims as flat token sequences, yet overlooking the dependency hierarchy among claims. Incorporating the hierarchy into self-attention poses two challenges. First, claim dependencies involve relation types with varying reliability: treating them indiscriminately allows noisy technical relations to corrupt cleaner legal citation signals. Second, when the dependency graph is defined over claims, Transformer models fail as they operate at the token level; broadcasting claim-level adjacency can dilute structural information across unrelated token pairs. A novel Patent Heterogeneous Attention Graph Encoder (PHAGE) addresses these challenges. To handle heterogeneous dependencies, PHAGE constructs a typed graph to separate legal citations from technical relations as distinct edge types.