4 citations · 7 across the 9 of their papers we have counts for
6 papers · 1 filter
Word Alignment as Preference for Machine Translation
Qiyu Wu, Masaaki Nagata, Zhongtao Miao +1
The problem of hallucination and omission, a long-standing problem in machine translation (MT), is more pronounced when a large language model (LLM) is used in MT because an LLM it…
Enhancing Cross-lingual Sentence Embedding for Low-resource Languages with Word Alignment
Zhongtao Miao, Qiyu Wu, Kaiyan Zhao +2
The field of cross-lingual sentence embeddings has recently experienced significant advancements, but research concerning low-resource languages has lagged due to the scarcity of p…
Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding
Kaiyan Zhao, Qiyu Wu, Xin-Qiang Cai +1
Learning multi-lingual sentence embeddings is a fundamental task in natural language processing. Recent trends in learning both mono-lingual and multi-lingual sentence embeddings a…
WSPAlign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction
Qiyu Wu, Masaaki Nagata, Yoshimasa Tsuruoka
Most existing word alignment methods rely on manual alignment datasets or parallel corpora, which limits their usefulness. Here, to mitigate the dependence on manual data, we broad…
PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings
Qiyu Wu, Chongyang Tao, Tao Shen +3
Learning sentence embeddings in an unsupervised manner is fundamental in natural language processing. Recent common practice is to couple pre-trained language models with unsupervi…
Taking Notes on the Fly Helps BERT Pre-training
Qiyu Wu, Chen Xing, Yatao Li +3
How to make unsupervised language pre-training more efficient and less resource-intensive is an important research direction in NLP. In this paper, we focus on improving the effici…