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20162024
most citedERNIE: Enhanced Representation through Knowledge Integration

773 citations · 1.2k across the 35 of their papers we have counts for

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44 papers · 1 filter

cs.CL20242 cited

Towards Boosting Many-to-Many Multilingual Machine Translation with Large Language Models

Pengzhi Gao, Zhongjun He, Hua Wu +1

The training paradigm for machine translation has gradually shifted, from learning neural machine translation (NMT) models with extensive parallel corpora to instruction finetuning…

cs.CL2023

An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text Translation

Pengzhi Gao, Ruiqing Zhang, Zhongjun He +2

Consistency regularization methods, such as R-Drop (Liang et al., 2021) and CrossConST (Gao et al., 2023), have achieved impressive supervised and zero-shot performance in the neur…

cs.CL2023

Learning Multilingual Sentence Representations with Cross-lingual Consistency Regularization

Pengzhi Gao, Liwen Zhang, Zhongjun He +2

Multilingual sentence representations are the foundation for similarity-based bitext mining, which is crucial for scaling multilingual neural machine translation (NMT) system to mo…

cs.CL2023

Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularization

Pengzhi Gao, Liwen Zhang, Zhongjun He +2

The multilingual neural machine translation (NMT) model has a promising capability of zero-shot translation, where it could directly translate between language pairs unseen during…

cs.CL20221 cited

Clip-Tuning: Towards Derivative-free Prompt Learning with a Mixture of Rewards

Yekun Chai, Shuohuan Wang, Yu Sun +3

Derivative-free prompt learning has emerged as a lightweight alternative to prompt tuning, which only requires model inference to optimize the prompts. However, existing work did n…

cs.CL20226 cited

ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding

Qiming Peng, Yinxu Pan, Wenjin Wang +12

Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding. However, most existing methods lack the systematic mining and u…