activity
20152026
most citedSemi-Supervised Learning for Neural Machine Translation

61 citations · 222 across the 20 of their papers we have counts for

collaborators

21 papers

cs.CL2026★ 2 cited

ERNIE 5.0 Technical Report

Haifeng Wang, Hua Wu, Tian Wu +432

In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…

cs.CL2025

AlignX: Advancing Multilingual Large Language Models with Multilingual Representation Alignment

Mengyu Bu, Shaolei Zhang, Zhongjun He +2

Multilingual large language models (LLMs) possess impressive multilingual understanding and generation capabilities. However, their performance and cross-lingual alignment often la…

cs.CL2025

Shall Your Data Strategy Work? Perform a Swift Study

Minlong Peng, Jingyi Yang, Zhongjun He +1

This work presents a swift method to assess the efficacy of particular types of instruction-tuning data, utilizing just a handful of probe examples and eliminating the need for mod…

cs.CL2024★ 2 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…