activity
20182022
most citedSemantic Neural Machine Translation using AMR

83 citations · 198 across the 37 of their papers we have counts for

collaborators

55 papers

cs.CL2022

A Graph Enhanced BERT Model for Event Prediction

Li Du, Xiao Ding, Yue Zhang +3

Predicting the subsequent event for an existing event context is an important but challenging task, as it requires understanding the underlying relationship between events. Previou…

cs.LG2022

NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting

Linyi Yang, Jiazheng Li, Ruihai Dong +2

Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvement…

cs.CL2021

Solving Aspect Category Sentiment Analysis as a Text Generation Task

Jian Liu, Zhiyang Teng, Leyang Cui +2

Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-s…

cs.CL20213 cited

Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation

Leyang Cui, Yu Wu, Shujie Liu +1

Although pre-training models have achieved great success in dialogue generation, their performance drops dramatically when the input contains an entity that does not appear in pre-…

cs.CL2021

Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation

Leonardo F. R. Ribeiro, Jonas Pfeiffer, Yue Zhang +1

Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR. However, this assumes a high quality of generate…

cs.CL20212 cited

Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoning

Cunxiang Wang, Boyuan Zheng, Yuchen Niu +1

To quantitatively and intuitively explore the generalization ability of pre-trained language models (PLMs), we have designed several tasks of arithmetic and logical reasoning. We b…