5 citations · 9 across the 5 of their papers we have counts for
5 papers
Large Language Models are Diverse Role-Players for Summarization Evaluation
Ning Wu, Ming Gong, Linjun Shou +2
Text summarization has a wide range of applications in many scenarios. The evaluation of the quality of the generated text is a complex problem. A big challenge to language evaluat…
Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding
Shining Liang, Linjun Shou, Jian Pei +4
Despite the great success of spoken language understanding (SLU) in high-resource languages, it remains challenging in low-resource languages mainly due to the lack of labeled trai…
Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition
Shining Liang, Ming Gong, Jian Pei +4
Named entity recognition (NER) is a fundamental component in many applications, such as Web Search and Voice Assistants. Although deep neural networks greatly improve the performan…
CalibreNet: Calibration Networks for Multilingual Sequence Labeling
Shining Liang, Linjun Shou, Jian Pei +3
Lack of training data in low-resource languages presents huge challenges to sequence labeling tasks such as named entity recognition (NER) and machine reading comprehension (MRC).…
A Multi-level Neural Network for Implicit Causality Detection in Web Texts
Shining Liang, Wanli Zuo, Zhenkun Shi +3
Mining causality from text is a complex and crucial natural language understanding task corresponding to the human cognition. Existing studies at its solution can be grouped into t…