1 code implementation • 11 Jan 2024 • Pengzhi Gao, Zhongjun He, Hua Wu, Haifeng Wang
The training paradigm for machine translation has gradually shifted, from learning neural machine translation (NMT) models with extensive parallel corpora to instruction finetuning on multilingual large language models (LLMs) with high-quality translation pairs.
1 code implementation • 28 Aug 2023 • Pengzhi Gao, Ruiqing Zhang, Zhongjun He, Hua Wu, Haifeng Wang
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 neural machine translation (NMT) field.
1 code implementation • 12 Jun 2023 • Pengzhi Gao, Liwen Zhang, Zhongjun He, Hua Wu, Haifeng Wang
Multilingual sentence representations are the foundation for similarity-based bitext mining, which is crucial for scaling multilingual neural machine translation (NMT) system to more languages.
1 code implementation • 12 May 2023 • Pengzhi Gao, Liwen Zhang, Zhongjun He, Hua Wu, Haifeng Wang
The experimental analysis also proves that CrossConST could close the sentence representation gap and better align the representation space.
1 code implementation • NAACL 2022 • Pengzhi Gao, Zhongjun He, Hua Wu, Haifeng Wang
We introduce Bi-SimCut: a simple but effective training strategy to boost neural machine translation (NMT) performance.
Ranked #1 on Machine Translation on WMT2014 German-English
no code implementations • Findings (EMNLP) 2021 • Jicheng Li, Pengzhi Gao, Xuanfu Wu, Yang Feng, Zhongjun He, Hua Wu, Haifeng Wang
To further improve the faithfulness and diversity of the translations, we propose two simple but effective approaches to select diverse sentence pairs in the training corpus and adjust the interpolation weight for each pair correspondingly.
1 code implementation • EMNLP 2020 • Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Haoying Zhang, Xiaodan Liang, Teruko Mitamura, Eric P. Xing, Zhiting Hu
Empirical natural language processing (NLP) systems in application domains (e. g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization.