5 citations · 14 across the 8 of their papers we have counts for
7 papers · 1 filter
Unified Demonstration Retriever for In-Context Learning
Xiaonan Li, Kai Lv, Hang Yan +6
In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction.…
Candidate Soups: Fusing Candidate Results Improves Translation Quality for Non-Autoregressive Translation
Huanran Zheng, Wei Zhu, Pengfei Wang +1
Non-autoregressive translation (NAT) model achieves a much faster inference speed than the autoregressive translation (AT) model because it can simultaneously predict all tokens du…
A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation
Tianxiang Sun, Xiangyang Liu, Wei Zhu +7
Early exiting allows instances to exit at different layers according to the estimation of difficulty. Previous works usually adopt heuristic metrics such as the entropy of internal…
Lex-BERT: Enhancing BERT based NER with lexicons
Wei Zhu, Daniel Cheung
In this work, we represent Lex-BERT, which incorporates the lexicon information into Chinese BERT for named entity recognition (NER) tasks in a natural manner. Instead of using wor…
CMV-BERT: Contrastive multi-vocab pretraining of BERT
Wei Zhu, Daniel Cheung
In this work, we represent CMV-BERT, which improves the pretraining of a language model via two ingredients: (a) contrastive learning, which is well studied in the area of computer…
MVP-BERT: Redesigning Vocabularies for Chinese BERT and Multi-Vocab Pretraining
Wei Zhu
Despite the development of pre-trained language models (PLMs) significantly raise the performances of various Chinese natural language processing (NLP) tasks, the vocabulary for th…