Qianli Ma

TL;DR: The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed.

TL;DR: This work proposes a unified causal framework to retrieve the causality from both new entity types and Other -Class and applies curriculum learning to mitigate the impact of label noise and introduce a self-adaptive weight for balancing the causal effects between new entitytypes and Other-Class.

TL;DR: In this paper, a PageRank-based random propagation strategy for data augmentation is designed and a graph regularization term is designed, which can find more useful information for classification results from neighbor nodes to improve the performance of the model.

TL;DR: By representing label relationships as graphs, this work formulate cross-domain NER as a graph matching problem and shows that the proposed method outperforms a series of transfer learning, multi-task learning, and few-shot learning methods.

TL;DR: Face-to-Face Contrastive Learning (F2F-CL), a graph neural network designed to model social interactions using factorization nodes to contextualize the multimodal face- to-face interaction along the boundaries of the speaking turn is proposed.