22 citations · 28 across the 5 of their papers we have counts for
8 papers
Relational Surrogate Loss Learning
Tao Huang, Zekang Li, Hua Lu +6
Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e.g., average precision and F1 score. Thi…
Mental Health Assessment for the Chatbots
Yong Shan, Jinchao Zhang, Zekang Li +2
Previous researches on dialogue system assessment usually focus on the quality evaluation (e.g. fluency, relevance, etc) of responses generated by the chatbots, which are local and…
Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances
Zekang Li, Jinchao Zhang, Zhengcong Fei +2
Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained language models. However, they gene…
WeChat AI & ICT's Submission for DSTC9 Interactive Dialogue Evaluation Track
Zekang Li, Zongjia Li, Jinchao Zhang +2
We participate in the DSTC9 Interactive Dialogue Evaluation Track (Gunasekara et al. 2020) sub-task 1 (Knowledge Grounded Dialogue) and sub-task 2 (Interactive Dialogue). In sub-ta…
A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking
Yong Shan, Zekang Li, Jinchao Zhang +4
Recent studies in dialogue state tracking (DST) leverage historical information to determine states which are generally represented as slot-value pairs. However, most of them have…
Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization
Zeyang Lei, Zekang Li, Jinchao Zhang +5
Recently, variational auto-encoder (VAE) based approaches have made impressive progress on improving the diversity of generated responses. However, these methods usually suffer the…