70 citations · 123 across the 8 of their papers we have counts for
8 papers
OntoZSL: Ontology-enhanced Zero-shot Learning
Yuxia Geng, Jiaoyan Chen, Zhuo Chen +5
Zero-shot Learning (ZSL), which aims to predict for those classes that have never appeared in the training data, has arisen hot research interests. The key of implementing ZSL is t…
Exploiting Behavioral Consistence for Universal User Representation
Jie Gu, Feng Wang, Qinghui Sun +4
User modeling is critical for developing personalized services in industry. A common way for user modeling is to learn user representations that can be distinguished by their inter…
Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement
Xingjian Li, Di Hu, Xuhong Li +5
Fine-tuning deep neural networks pre-trained on large scale datasets is one of the most practical transfer learning paradigm given limited quantity of training samples. To obtain b…
Generative Adversarial Zero-shot Learning via Knowledge Graphs
Yuxia Geng, Jiaoyan Chen, Zhuo Chen +4
Zero-shot learning (ZSL) is to handle the prediction of those unseen classes that have no labeled training data. Recently, generative methods like Generative Adversarial Networks (…
Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models
Zhi-Xiu Ye, Qian Chen, Wen Wang +1
The state-of-the-art pre-trained language representation models, such as Bidirectional Encoder Representations from Transformers (BERT), rarely incorporate commonsense knowledge or…
Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
Zhi-Xiu Ye, Zhen-Hua Ling
This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. Previous studies on this topic adopt prototypical networks, which c…
Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
Zhi-Xiu Ye, Zhen-Hua Ling
This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision. Previous studies mainly focus on sentence-level de-no…
Hybrid semi-Markov CRF for Neural Sequence Labeling
Zhi-Xiu Ye, Zhen-Hua Ling
This paper proposes hybrid semi-Markov conditional random fields (SCRFs) for neural sequence labeling in natural language processing. Based on conventional conditional random field…