no code implementations • WMT (EMNLP) 2021 • Yu Wan, Dayiheng Liu, Baosong Yang, Tianchi Bi, Haibo Zhang, Boxing Chen, Weihua Luo, Derek F. Wong, Lidia S. Chao
After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.
no code implementations • CL (ACL) 2022 • Yu Wan, Baosong Yang, Derek Fai Wong, Lidia Sam Chao, Liang Yao, Haibo Zhang, Boxing Chen
After empirically investigating the rationale behind this, we summarize two challenges in NMT for STs associated with translation error types above, respectively: (1) the imbalanced length distribution in training set intensifies model inference calibration over STs, leading to more over-translation cases on STs; and (2) the lack of contextual information forces NMT to have higher data uncertainty on short sentences, and thus NMT model is troubled by considerable mistranslation errors.
no code implementations • 28 Nov 2023 • Zixiang Zhou, Yu Wan, Baoyuan Wang
The field has made significant progress in synthesizing realistic human motion driven by various modalities.
no code implementations • 28 Nov 2023 • Zixiang Zhou, Yu Wan, Baoyuan Wang
AvatarGPT treats each task as one type of instruction fine-tuned on the shared LLM.
1 code implementation • 12 Jul 2023 • Xiangpeng Wei, Haoran Wei, Huan Lin, TianHao Li, Pei Zhang, Xingzhang Ren, Mei Li, Yu Wan, Zhiwei Cao, Binbin Xie, Tianxiang Hu, Shangjie Li, Binyuan Hui, Bowen Yu, Dayiheng Liu, Baosong Yang, Fei Huang, Jun Xie
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions.
no code implementations • 17 Feb 2023 • Keqin Bao, Yu Wan, Dayiheng Liu, Baosong Yang, Wenqiang Lei, Xiangnan He, Derek F. Wong, Jun Xie
In this paper, we propose Fine-Grained Translation Error Detection (FG-TED) task, aiming at identifying both the position and the type of translation errors on given source-hypothesis sentence pairs.
1 code implementation • 18 Oct 2022 • Yu Wan, Keqin Bao, Dayiheng Liu, Baosong Yang, Derek F. Wong, Lidia S. Chao, Wenqiang Lei, Jun Xie
In this report, we present our submission to the WMT 2022 Metrics Shared Task.
1 code implementation • 18 Oct 2022 • Keqin Bao, Yu Wan, Dayiheng Liu, Baosong Yang, Wenqiang Lei, Xiangnan He, Derek F. Wong, Jun Xie
In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation).
no code implementations • 28 Apr 2022 • Yu Wan, Dayiheng Liu, Baosong Yang, Tianchi Bi, Haibo Zhang, Boxing Chen, Weihua Luo, Derek F. Wong, Lidia S. Chao
After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.
1 code implementation • Findings (ACL) 2022 • Yu Wan, Baosong Yang, Dayiheng Liu, Rong Xiao, Derek F. Wong, Haibo Zhang, Boxing Chen, Lidia S. Chao
Attention mechanism has become the dominant module in natural language processing models.
2 code implementations • ACL 2022 • Yu Wan, Dayiheng Liu, Baosong Yang, Haibo Zhang, Boxing Chen, Derek F. Wong, Lidia S. Chao
Translation quality evaluation plays a crucial role in machine translation.
no code implementations • 1 Mar 2022 • Yidan Zhang, Yu Wan, Dayiheng Liu, Baosong Yang, Zhenan He
Recently, Minimum Bayes Risk (MBR) decoding has been proposed to improve the quality for NMT, which seeks for a consensus translation that is closest on average to other candidates from the n-best list.
1 code implementation • EMNLP 2020 • Yu Wan, Baosong Yang, Derek F. Wong, Yikai Zhou, Lidia S. Chao, Haibo Zhang, Boxing Chen
Recent studies have proven that the training of neural machine translation (NMT) can be facilitated by mimicking the learning process of humans.
no code implementations • ACL 2020 • Yikai Zhou, Baosong Yang, Derek F. Wong, Yu Wan, Lidia S. Chao
We propose uncertainty-aware curriculum learning, which is motivated by the intuition that: 1) the higher the uncertainty in a translation pair, the more complex and rarer the information it contains; and 2) the end of the decline in model uncertainty indicates the completeness of current training stage.
2 code implementations • 11 Dec 2019 • Yu Wan, Baosong Yang, Derek F. Wong, Lidia S. Chao, Haihua Du, Ben C. H. Ao
As a special machine translation task, dialect translation has two main characteristics: 1) lack of parallel training corpus; and 2) possessing similar grammar between two sides of the translation.