no code implementations • WMT (EMNLP) 2020 • Qu Cui, Xiang Geng, ShuJian Huang, Jiajun Chen
This paper describes our system of the sentence-level and word-level Quality Estimation Shared Task of WMT20.
no code implementations • WMT (EMNLP) 2021 • Yimeng Chen, Chang Su, Yingtao Zhang, Yuxia Wang, Xiang Geng, Hao Yang, Shimin Tao, Guo Jiaxin, Wang Minghan, Min Zhang, Yujia Liu, ShuJian Huang
This paper presents our work in WMT 2021 Quality Estimation (QE) Shared Task.
no code implementations • 21 Mar 2024 • Haofei Zhao, Yilun Liu, Shimin Tao, Weibin Meng, Yimeng Chen, Xiang Geng, Chang Su, Min Zhang, Hao Yang
Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations, which is of great importance for the development of MT.
1 code implementation • 12 Jan 2024 • Shuaijie She, Wei Zou, ShuJian Huang, Wenhao Zhu, Xiang Liu, Xiang Geng, Jiajun Chen
To enhance reasoning abilities in non-dominant languages, we propose a Multilingual-Alignment-as-Preference Optimization framework (MAPO), aiming to align the reasoning processes in other languages with the dominant language.
1 code implementation • 12 Jan 2024 • Xu Huang, Zhirui Zhang, Xiang Geng, Yichao Du, Jiajun Chen, ShuJian Huang
Large Language Models (LLMs) have achieved remarkable results in the machine translation evaluation task, yet there remains a gap in knowledge regarding how they utilize the provided data to conduct evaluations.
1 code implementation • 23 Sep 2023 • Xiang Geng, Zhejian Lai, Yu Zhang, Shimin Tao, Hao Yang, Jiajun Chen, ShuJian Huang
We generate pseudo MQM data using parallel data from the WMT translation task.
1 code implementation • 3 Dec 2022 • Shuaijie She, Xiang Geng, ShuJian Huang, Jiajun Chen
To separate the preference for factual consistency, we propose an unsupervised framework named CoP by controlling the preference of the generation model with the help of prompt.
no code implementations • 15 May 2021 • Qu Cui, ShuJian Huang, Jiahuan Li, Xiang Geng, Zaixiang Zheng, Guoping Huang, Jiajun Chen
However, we argue that there are gaps between the predictor and the estimator in both data quality and training objectives, which preclude QE models from benefiting from a large number of parallel corpora more directly.
1 code implementation • 17 Feb 2021 • Bin Gu, Guodong Liu, yanfu Zhang, Xiang Geng, Heng Huang
Modern machine learning algorithms usually involve tuning multiple (from one to thousands) hyperparameters which play a pivotal role in terms of model generalizability.
no code implementations • 29 Jul 2019 • Wanli Shi, Bin Gu, Xiang Li, Xiang Geng, Heng Huang
To address this problem, in this paper, we propose a novel scalable quadruply stochastic gradient algorithm (QSG-S2AUC) for nonlinear semi-supervised AUC optimization.
no code implementations • 26 Jul 2019 • Xiang Geng, Bin Gu, Xiang Li, Wanli Shi, Guansheng Zheng, Heng Huang
Specifically, to handle two types of data instances involved in S$^3$VM, TSGS$^3$VM samples a labeled instance and an unlabeled instance as well with the random features in each iteration to compute a triply stochastic gradient.