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Less is More: on the Over-Globalizing Problem in Graph Transformers
Then we propose a novel Bi-Level Global Graph Transformer with Collaborative Training (CoBFormer), including the inter-cluster and intra-cluster Transformers, to prevent the over-globalizing problem while keeping the ability to extract valuable information from distant nodes.
Graph Fairness Learning under Distribution Shifts
Recently, there has been an increasing interest in ensuring fairness on GNNs, but all of them are under the assumption that the training and testing data are under the same distribution, i. e., training data and testing data are from the same graph.
Evaluating and Improving Context Attention Distribution on Multi-Turn Response Generation using Self-Contained Distractions
In this paper, we focus on an essential component of multi-turn generation-based conversational agents: context attention distribution, i. e. how systems distribute their attention on dialogue's context.
Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation
Furthermore, we propose Domain-specific Frequency (DF), a novel word-level importance weight that measures the relative importance of a word for a specific corpus compared to other corpora.
Automatic Evaluation of Neural Personality-based Chatbots
Stylistic variation is critical to render the utterances generated by conversational agents natural and engaging.
