As a connection, the scale and the performance of the teacher assistant is crucial for transferring the knowledge from the teacher to the student.
To enhance the performance of dense retrieval models without loss of efficiency, we propose a GNN-encoder model in which query (passage) information is fused into passage (query) representations via graph neural networks that are constructed by queries and their top retrieved passages.
Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages.
Recent studies about learning multilingual representations have achieved significant performance gains across a wide range of downstream cross-lingual tasks.
Specifically, we use graph convolutions to learn the structural and functional joint embedding, where the graph structure is defined with structural connectivity and node features are from the functional connectivity.
Generating an article automatically with computer program is a challenging task in artificial intelligence and natural language processing.