27 citations · 66 across the 18 of their papers we have counts for
8 papers · 1 filter
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning
Ran Zhou, Xin Li, Lidong Bing +2
In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data. However, due to sub…
History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System
Tong Zhang, Yong Liu, Boyang Li +5
With the evolution of pre-trained language models, current open-domain dialogue systems have achieved great progress in conducting one-session conversations. In contrast, Multi-Ses…
Multi-Aspect Explainable Inductive Relation Prediction by Sentence Transformer
Zhixiang Su, Di Wang, Chunyan Miao +1
Recent studies on knowledge graphs (KGs) show that path-based methods empowered by pre-trained language models perform well in the provision of inductive and explainable relation p…
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
Qihuang Zhong, Liang Ding, Yibing Zhan +11
This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard. SuperGLUE is more challenging than the widely used general language…
ConNER: Consistency Training for Cross-lingual Named Entity Recognition
Ran Zhou, Xin Li, Lidong Bing +3
Cross-lingual named entity recognition (NER) suffers from data scarcity in the target languages, especially under zero-shot settings. Existing translate-train or knowledge distilla…
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER
Ran Zhou, Xin Li, Ruidan He +4
Data augmentation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as NER, data augmentation methods often suffe…
Towards Persona-Based Empathetic Conversational Models
Peixiang Zhong, Chen Zhang, Hao Wang +2
Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. In Psychology, persona has been shown to be highly correlated t…
Converse Attention Knowledge Transfer for Low-Resource Named Entity Recognition
Shengfei Lyu, Linghao Sun, Huixiong Yi +3
In recent years, great success has been achieved in many tasks of natural language processing (NLP), e.g., named entity recognition (NER), especially in the high-resource language,…