14 citations · 21 across the 6 of their papers we have counts for
6 papers
Robust LLM Unlearning Against Relearning Attacks: The Minor Components in Representations Matter
Zeguan Xiao, Xuanzhe Xu, Yun Chen +5
Large language model (LLM) unlearning aims to remove specific data influences from pre-trained model without costly retraining, addressing privacy, copyright, and safety concerns.…
Multilingual Sentence Transformer as A Multilingual Word Aligner
Weikang Wang, Guanhua Chen, Hanqing Wang +2
Multilingual pretrained language models (mPLMs) have shown their effectiveness in multilingual word alignment induction. However, these methods usually start from mBERT or XLM-R. I…
Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation
Guanhua Chen, Shuming Ma, Yun Chen +4
This paper demonstrates that multilingual pretraining and multilingual fine-tuning are both critical for facilitating cross-lingual transfer in zero-shot translation, where the neu…
Zero-shot Cross-lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders
Guanhua Chen, Shuming Ma, Yun Chen +5
Previous work mainly focuses on improving cross-lingual transfer for NLU tasks with a multilingual pretrained encoder (MPE), or improving the performance on supervised machine tran…
Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation
Wei Peng, Chongxuan Huang, Tianhao Li +2
Existing data augmentation approaches for neural machine translation (NMT) have predominantly relied on back-translating in-domain (IND) monolingual corpora. These methods suffer f…
Accurate Word Alignment Induction from Neural Machine Translation
Yun Chen, Yang Liu, Guanhua Chen +2
Despite its original goal to jointly learn to align and translate, prior researches suggest that Transformer captures poor word alignments through its attention mechanism. In this…