Despite achieving remarkable performance, previous knowledge-enhanced works usually only use a single-source homogeneous knowledge base of limited knowledge coverage.
Knowledge-enhanced methods have bridged the gap between human beings and machines in generating dialogue responses.
Specifically, we design the policy network in our model as a pseudo-siamese policy network that consists of two sub-policy networks.
The representations of entities and relations are learned via contrasting the positive and negative triplets.
This is attributed to the rich attribute information contained in KG to improve item and user representations as side information.
Recently, x-vector has been a successful and popular approach for speaker verification, which employs a time delay neural network (TDNN) and statistics pooling to extract speaker characterizing embedding from variable-length utterances.
To address this issue, we propose a new architecture, named dynamic multi-scale convolution, which consists of dynamic kernel convolution, local multi-scale learning, and global multi-scale pooling.
In this paper, we propose a Multi-Level Graph Contrastive Learning (MLGCL) framework for learning robust representation of graph data by contrasting space views of graphs.
Developing the model for temporal knowledge graphs completion is an increasingly important task.
In addition, we summarize three kinds of augmentation methods for graph-structured data and apply them to the DGB.
Given a query, our approach first retrieves a set of prototype dialogues that are relevant to the query.
We collect and build a large-scale Chinese dataset aligned with the commonsense knowledge for dialogue generation.
Therefore, we developed a model to predict and identify car crashes from surveillance cameras based on a 3D reconstruction of the road plane and prediction of trajectories.
Recent years have witnessed a surge of interest on response generation for neural conversation systems.
In this paper, we introduce MOBA-Slice, a time slice based evaluation framework of relative advantage between teams in MOBA games.