A Context-Aware Query Suggestion Method Based on Multi-source Data Augmentation through Cross-Attention
Most existing neural network-based approaches for query suggestion use solely query sequences in query logs as training data.However,these methods cannot fully mine and infer all kinds of semantic relationships among words or concepts from query sequences because queries in query sequences inherently suffer from a lack of syntactic relation,even a loss of semantics.To solve this problem,this paper proposes a new neural network model based on multi-source data augmentation through cross-attention(MDACA)for generating context-aware query suggestions.Proposed model adopts a Transformer-based encoder-decoder model that incorporates document-level semantics and global query suggestions into query-level information through cross-attention.The experimental results show that in contrast to the current suggestion models,the proposed model can generate context-aware query suggestions with higher relevance.
query suggestiondata augmentationcross-attentioncontext-awareTransformer model
万方数据
维普
