9 citations · 14 across the 4 of their papers we have counts for
5 papers
How to Use Parallel Data for Multilingual Pretraining
Zhihong Huang, Yao Lu, Derek F. Wong +2
High-quality human-translated parallel corpora can improve cross-lingual transfer, but are typically too small for large-scale pretraining. Machine-translated web corpora help over…
Anchor-based Large Language Models
Jianhui Pang, Fanghua Ye, Derek Fai Wong +3
Large language models (LLMs) predominantly employ decoder-only transformer architectures, necessitating the retention of keys/values information for historical tokens to provide co…
Benchmarking LLMs via Uncertainty Quantification
Fanghua Ye, Mingming Yang, Jianhui Pang +5
The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods. However, current evalu…
Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models
Jianhui Pang, Fanghua Ye, Longyue Wang +4
The evolution of Neural Machine Translation (NMT) has been significantly influenced by six core challenges (Koehn and Knowles, 2017), which have acted as benchmarks for progress in…
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task
Keqin Bao, Yu Wan, Dayiheng Liu +5
In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation). Specifically, our syst…