22 citations · 71 across the 10 of their papers we have counts for
4 papers · 1 filter
COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization
Chenxin An, Ming Zhong, Zhiyong Wu +3
Traditional training paradigms for extractive and abstractive summarization systems always only use token-level or sentence-level training objectives. However, the output summary i…
CoNT: Contrastive Neural Text Generation
Chenxin An, Jiangtao Feng, Kai Lv +3
Recently, contrastive learning attracts increasing interests in neural text generation as a new solution to alleviate the exposure bias problem. It introduces a sequence-level trai…
TURNER: The Uncertainty-based Retrieval Framework for Chinese NER
Zhichao Geng, Hang Yan, Zhangyue Yin +2
Chinese NER is a difficult undertaking due to the ambiguity of Chinese characters and the absence of word boundaries. Previous work on Chinese NER focus on lexicon-based methods to…
-Tuning: An Efficient Tuning Paradigm for Large-Scale Pre-Trained Models via Label Representation Learning
Yitao Liu, Chenxin An, Xipeng Qiu
With the success of large-scale pre-trained models (PTMs), how efficiently adapting PTMs to downstream tasks has attracted tremendous attention, especially for PTMs with billions o…