9 citations · 13 across the 5 of their papers we have counts for
5 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…
Modeling Coverage for Non-Autoregressive Neural Machine Translation
Yong Shan, Yang Feng, Chenze Shao
Non-Autoregressive Neural Machine Translation (NAT) has achieved significant inference speedup by generating all tokens simultaneously. Despite its high efficiency, NAT usually suf…
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…
Improving Bidirectional Decoding with Dynamic Target Semantics in Neural Machine Translation
Yong Shan, Yang Feng, Jinchao Zhang +2
Generally, Neural Machine Translation models generate target words in a left-to-right (L2R) manner and fail to exploit any future (right) semantics information, which usually produ…