China’s remarkable rise as an international technology and innovation powerhouse comes courtesy of domestic efforts to upgrade its scientific enterprise. In this brief, Cong Cao, a professor in innovation studies at Nottingham University Ningbo China, argues that the globalization of science has also played a significant role, fostering links between Chinese and international researchers, allowing Chinese students to study abroad, and attracting foreign direct investment to China’s research sector. However, as pressures in Western countries to decouple from China mount, the future of China’s science, technology, and innovation system faces strong headwinds.

Dialogue State Tracking (DST), a key component of task-oriented dialogue systems, tracks user intentions by predicting the values of pre-defined slots in a dialogue. Existing works on DST treat all slots indiscriminately and independently, which ignores the relationships across slots and limits the learning of hard slots (those slots are hard to be predicted correctly), eventually hurting overall performance. In this paper, we propose an iterative learning framework, i.e. iteratively updates the dialogue state with confident slots, to alleviate the aforementioned problem. Specifically, we first employ a scorer to estimate slot confidence. Then, those slots with high confidence are utilized to update the previous state, and the updated state will be fed into the scorer again to recalculate the confidence. In the last iteration, we apply an objective with the confidence penalty to focus on the hard slots. The experiments show that our approach outperforms existing methods on popular datasets.