3 citations · 7 across the 7 of their papers we have counts for
7 papers
A Safe and Efficient Self-evolving Algorithm for Decision-making and Control of Autonomous Driving Systems
Shuo Yang, Liwen Wang, Yanjun Huang +1
Autonomous vehicles with a self-evolving ability are expected to cope with unknown scenarios in the real-world environment. Take advantage of trial and error mechanism, reinforceme…
Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations
Jiachi Liu, Liwen Wang, Guanting Dong +8
In real dialogue scenarios, as there are unknown input noises in the utterances, existing supervised slot filling models often perform poorly in practical applications. Even though…
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
Guanting Dong, Zechen Wang, Jinxu Zhao +10
The objective of few-shot named entity recognition is to identify named entities with limited labeled instances. Previous works have primarily focused on optimizing the traditional…
Multiple-level Green Noise Mask Design for Practical Fourier Phase Retrieval
Qiuliang Ye, Bingo Wing-Kuen Ling, Li-Wen Wang +1
Phase retrieval, a long-established challenge for recovering a complex-valued signal from its Fourier intensity measurements, has attracted significant interest because of its far-…
Revisit Out-Of-Vocabulary Problem for Slot Filling: A Unified Contrastive Frameword with Multi-level Data Augmentations
Daichi Guo, Guanting Dong, Dayuan Fu +9
In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced generalization facing Out-of-Vocabulary (OOV) prob…
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition
Guanting Dong, Zechen Wang, Liwen Wang +10
Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Most existing prototype-based sequence labeling models tend to memor…